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Clustering (20.01.2011)
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00:00
Sat around and who had a ruined and welcome to lecture that housing and said that my any and again this will be of interest to lecture because today weaker and take a deep dive into the world of last during last week were talking about classification which is 1 way of attaching labels to dead on the sudden death said of the of the items and you want to know all which of them be to get which is verisimilitude Clustering in a way that only the club's during a you find group of objects that are some all similar to each other but you I don't really like the classification your try to find out what worked and that land of the with problem misses decision to these where you off questions and the mood of the treaty I from certain Omnes attributer on the characteristics of the cell is the number bigger than 40 Yesil some the and then you Brown child and the Tree on to you get homogeneously notes on well world that L some some specific Kyneton giving in to the kind and and the interesting for a 2nd that is the not even based classified way of probability mobile that they basic moving tells us what to order so that has to know about them in the UK about the classes and the last thing that we were talking about is that was appalled to machines and basically a binary classified tries to find a separating hyperplanes bomb killed well distinguish between 2 classes of of all points in the dam and place in a cash so this week we want to talk about frustrating Clustering basically means unless supervised running the classification tusks this provides churning class designer labels to certain points in the training centre and from these labels you're Welch characteristics select attributer value as part of a certain class usually hat and was not which you can test public and into was under supervised learning you don't have any labels you don't know which point longstalled not it does have the distribution of points in the state and then have to find out what points on similar what point less similar where would be good are the well hyperplane for the take to to distinguish between points and will be basically tell you all we know about the strength in the lecture of starting with flat after being algorithms so difficult is became means algorithm a Hierarchical Clustering where we get such fancy Dendrograms will talk about outlined for a bit of finding out how good a class straight losses quality medals for Clustering and and that will go into laughing for that are where houses where that a usually is very highdemand and that is a bit of a self confidence that said the simplest learning the only Labels attached to some of the objects that trading set to learn what we can get out of the the pretty classified objects in terms of I'm off to pick up a tribute values to the characteristics of and that is basically the idea classification during classified trained classified the and classified that is used on that that he had never seen before and assigned sees the status of the cost and the contrasting approaches uncivilised well with the simplest of this unless of the and that later classification I'd just seed the that items all points in space with a attribute values and that they got that would be good groupings of the study and that this was known as just have to find a compact and probably 97 fitting Clusters so you can distinguish between different that became saving again and a new book for the CIA with the sale and the cost showing algorithms can be a distinguished and into a couple of classes and 1 of the major classes is that the current rules where we just come to the point space as a whole the number of points you at the same time and tried to make out of what is for what is not knowing with her
06:15
Cook who was redomicile picking up the the savings ball to you by the So that we get the new so we need to find an internist structure the and ladybirds that the who had been and and the exit just is the premises of organising objects in such a way that objects that are put in a single clustered have a shed some some large number of at tributes the given any simulating measure that will be considered similar similar that objects not just could not which led him safe for a minute to find out what the savings of the world and held on to the next slide
07:40
In the cause of 1 of window for the suffering of actually a city that we learn from the early on on all if you think about the early part animals stuff animals that he had as a child and like but they were frightened they would come and variety shapes and babies really quickly learned to distinguish between the 2 noticed that some of them off to a bright pink and some of them are baby blue or whatever and that most of them are kind of like having a very big earnings and but the of the of something like that but to make them to to make them cuddly to make distinguish from all walks to anything else it and this promises of distinguishing between its the refined in the human brain which is really a new a network that is put up with the cost if we consider all the photos of the Year which bags of those over here but I can't published meeting in terms of colours of that could be more dissimilar they have a kind of like eaten away and maybe the odd is kind of like interesting to Noach and in the teeth of full of Dogville what what she is
09:13
But let us distinguish between captain thought everybody will probably agree that this year is why what makes a ticket world has some 1st baulked also the walks was kept like about and Indians faces a slightly triangular Pauline which seems to be true for most and worse the take up of this is that all the UK for its to a cap of below the body and the positions that receive screams that led and that is exactly what happened which can be too sure about the singer attributed the at tribute that the finds a care of distinguished cat from abroad but its basic but led to a tribute to get was certain waiting scheme that we have voiced Uganda what even if but the face is not true triangular enough stood at the foot account for along the less than those of country lot them that whatever you the week's Oviatt the tributes that we noticed that the 2 seed and then try to find out from the bench with a tribute was the best classification was the best fast to put it in and we will follow probably disciples kept found
11:13
But the basic human the far flock last spring is the segmentation off that and the it as it is very often used in the Indian market research or Patten recognition for a financial services for the under whenever we of market research we groups and went to figure out what are the start of the World also off all for some services when we do that and that the problem might not be to interested in segmenting people from the start but just looking at the death of finding out how API Web obvious segment strike at but also in information road trip which possessing plays a big part and and this is very interesting to know what that for example face recognitional some techniques are mainly built on And that's going to Mockett segmentations from the blows to stop divide into a subset of customers that should some attributes of for example the young custom made not actually beat the should be happy like getting custom a should be very open to what novelties they should up like bright colours that they should be unafraid of of technology or something like that and with the Charities at review that the group of custom a group of customers that is much better described in that way that would be be described as saying what they are basically aged 15 to 24 are high and that is young cost actually in this age group you will find some very conservative buyers and you may find some very open by some very young by the end of the age group of sometimes even seniors technology oriented justified failing around with new technology which is typically a sign of a young by and that is an interesting thing to do not to do 2 to just segregate them by some attributed the just finding out what are the pros and cons and and in all the different dimensions of some 1st so that I do it is you collect different at tributes of customers based on June graphic demographic lifestyle and for each usage information over service inflammation of what would be happy to fight classes of some of the cost customers and after the middle quality of your Clustering by assessing the predicted part of your Clustering because of a new comes customer comes into the game and I can't directly put him in 1 Cluster so the spected buying be the expected of well what he buys helmet to buy the stuff that should reflect the averages last and if you have a good Clustering that will work and action the hit in Germany for example of the back system probably most of you unknown about that only people using payback time that kind of a kind of a rural systems and Ali you buy something of a back of about this was several shops I'm of bands over several shops and and and basically what the company get out of it is that they return to the bit of the prices to you so you get a little bit of money at the end of each month and they get your mockup of what you buy what distinctive and glad to be here and to the mistakes demographic like and this is where the right to cost ring Clustering from make Mockett studies of which than in terms of paying less than what they pay all the people part dissipating paid programme this is how it works basic and many people use and if you do not and other this this summer 1 of the 1st obviously Scalability by can find out the perfect after it takes me 100 year full thousand customers problem with blood but we need to be false and especially when talking about that a way of millions of entry for use which for for example selfwilled services order and you need a custom algorithms these costs and probably classes in real time and then in which to do so that different types of tributes may be fined a rich Putes yet that maybe numbers in terms of age group sold over time it may be of BBC room well made female something that you know like some categorical and that may be what kind of that in the and end
16:51
You need to think about the shape of what you want most closely tend to have around the shape alike find find find that the last but sometimes not sometimes you find something like a minimum bounding rectangle all but it may find some segments of the origin of access depends on the purpose and the last ball is dealing with the noisy that valued and expect that all the dead that you have the perfect but there will be outlawed there will be some noise and you have to let out to get a real Only 1 of the main challenges is of a high dimensionality the usually remained if you if take demographic data is a let things you have to graphical I'm made of female the age group of my name salt water but may be not and beauty or 20 30 40 demand which for most of reduces the foetus Seaview challenge the for example that a bases indexing works well on Tilton 12 times that of the look of I know dimensionality strike things off to a well had Virginia genius away to the 1st 4 different them time and so you might need a reduction of Diamond or you might need to focus on some specific review please the might of and other review something and another challenge is to blame but you don't have and you might have a few Integration women tend to sold into by the sprawled of males and to buy the prop but they often you will find that looking at the best your at so you your Clustering Crosas should be skewed by the death not something that some semantics that you put into that some of the difficult determined from way said and of these isn't through from a closest ally to produce to want to use let plus strings of space or or or kick of pasta and a flight of classes bigger Clouseau's which may have stopped last bomb went hobble staff who want to beat each deltic part of a single closer Meij some objects of the ship but sort of high to know about the quality of life for the 2 people say out of the way the other person to a close so that way which once and for the measure and was also the question for the for the algorithm high would do you do with how the you arrive at the club and this is actually a bit of some of the questions that will try to answer in of the following I'm will when and the 1st prise is a highly class of the world could just collocate so the cake left at the top of the determining the extra time of it but it's all with the aim of the island and though there are basically 2 ways of getting the state law that loses rather autocratic and you just take a 5 4 7 0 for the you stop a classroom processes was having a certain amount of justice that you might need all but you will find helpful in March
21:18
And because in the end will have exactly the skate vessels that that you about wanted if you do not defined fixed case but that the number of class all where to break cost was a Apollo to fuse classes together depend on some well attributes of some pectoris 6 of that also find helps you will not be wrong with the tool's number of classes on the other hand everything might happen or like you made might end up with some big clustered which usually does not help you that you might end up the flowers the sides of the aisle plus but you come control any more which is probably an oral of setting off of what you might want to sell the rights choice of the number of class of early and the problem of that you want to sort of think about it if you like your consumers talkative Mockett and have a number of complaints from any Clusters would you want your customers to be there for a number of complaint that here mind that would be for all be less than 5 for more than 10 something wrong this this vulnerable because start off you have to distinguish and has to make sense to distinguish so thousand probably of that at the end on the other hand you have to handle the media to manage the so maybe 20 is a bad idea but you want to do as something specifically individually for each just so may be is a that but this is basically or you do it you hear heuristically to try to figure out what the right thing in the sentencing that are not pick up the flat after his good idea because you find the classes at what the you petition the Object into the cake last of that it found the hierarchical said he would stops with some of the
24:05
And then either if you start from the top of split the club's into smaller wants to if you start from the back to be with merged also stop was each object of being in individual Cluster and stop merging those that are very similar was was a graduate the you collect the objects from single Clusters into bigger clubs and the other those called devices thoroughly their object off in English early in a single big Foster and then use it to the last 4 of dissatisfied with his basically hierarchical Halstead plus for half classroom means that every customer every person every that items is assigned to exactly what cost and that is usually with the people do you don't want to talk to people because they might be long to 2 glasses all are basically difference between 2 places you don't talk to them twice you like by sending them different differently styled advertising Tito when we do that sometimes it can be around can be sent a sensible to do so she was Clustering but basically a you have distribution for each day object that the different class to the you just beyond led less to some cost and there might be some plausibly death don't belong to the might be some clubs the you belong to was probability of 20 per cent of the sample of and and so we are now ready to basically formulaic the problem Clustering we have a collection of customers for some extra that items were maybe we have decided for some type of plastic of salt and salt and we have not jective function that assigned and number for a possible clusters of the collection that will be the quality of the and Italy somehow we need to find a Clustering that my the of ticket function for that it might also be sensible to maximise the object of function as it is to find the other way around and found 1 when minimising the object of functions the quality of the book and the rest of the lecture found we exclude empty selfcritical was state or order because the but we want five set for because of the hot to slow to set up up a code of conduct and restraint from taking
27:17
And the only lowquality of across is measured by the subject to function not and a usually this update to to function is is a measure of distance from the tuition we might find a classroom is that if the objects in the cost of a simpler to each other and are likely they have to be very close to each of the 4 took it to a different classes with problem and them not to allow Lab all to be very close to each other rather Fault part so a and not take the function of quality function that called for local last in the plus the familiarity the between classicist method of low and high end Trott last summer summer's so inside each last December to of the height of the set up to give you an example of what would happen if we have this classroom here we just put them to bed and 1 plus the while those on a particular lead to to each other on the other hand those off particular solo a computer behind opted to function will find that the in truck plus the familiarity is not to impressed if you look at this Castronova it will find that the distances was in each of all very small firms may be the biggest of the match and that the celebrity between objects from different clubs is really very high of this seems to be a very good club and this is basically in tuition about all quality function and we might have some secondary goes as uses a very good idea to to avoid very the large Clusters nobody successfully contains 3 of nobody in the context of the last that 98 per cent of the population the of the separation of wealth of groups given that the that somehow distributed and an independent cache found every schools at least quality she talking about a use the internet Critias that the structure of the UK based on the notion of the Miller distant but there might also be stood up to it might find out that sometimes said good at it to have pile of experts that would Clustering and that the competitor to dispel Clustering out to find out how good your after will be with later of the not very of use because again it's a I'm a supervised a new arrival on to do a unsupervised 3 here in the eye in the sector have much more in the real said on the eve approach to trust Clustering would obviously be well at trial at all possible prostrate suppling or things together eye compute the from policy funky objective funky for each of the 1st race and then mapping the 1 with the highest but the easy but will would be expensive because I'm common clusterings on the eve of for 10 files for the million and new and the number of clusterings into exactly K Clusters is the most so called the numbers of the 2nd time and the strode lacked numbers are the exponential in the number of dead eye for you will find that the more that items you have having accident of the million or 10 Myr is immediately prohibited what that might be a way to deal with and objects but not with thousands more of with and this is what you for the way wait some from the rest we need some efficient and found although for the 1st kind of plastic of let us this 1 algorithm that really cut the mustard and that the case means probing the most well known of the strength of the pound the you used and was ever heard of paintings clusters of
32:37
The New and and 1 that we will talk about it came and went and all the petition around the manner and which is basically verisimilitude came means that you don't take me out of it cell high that work loses a hot fled Clustering at present that is every classes set of debt and the object no data point we don't to more than 1 in fact it lost to exactly and the number of Kelly the number of Clusters has to be defined in advance for the book came I need to know the and that point or usually just represented as pictures of for each attributer just take the number of out what every have the number of status you just teachers and play them into some numbers and you have a basic the real that effective and where I want to do it is you want to minimise the average distance from each other in the last 2 cost the only objects of the pasta and how do you measure the system are basically take the centre of gravity centre of each plastic and make sure that all of the points to be not too too far from the gravity account was centroid across the issue of the death said it might be didn't several classes and they will find that some of the objects of some of the crosses and he 0 6 points out with within SunTrust her was not the central of the centre was defined as the sent off gravity so that will be object new Cluster the respective distance of the centre is a minimum taking them pulled together
35:05
If the snows Radic area in the distance of over to the centre to give for example of the scent it with the other hand the centre he the met can also defined magically so you take over that a point that by the a in the in the Costa some up all want and you take component and some of the value novelised the comedy is basically the and rich in much the time APEC if you think to damage of that will be just the average of the spectacle of this is to say Kim difficult and Many of those arrested your some Scripps of Aclasta and the rescue of some of Where's is defined as the some at the distance of each point to the central squid basically you what do you needed for why would you be interested in or a city of some of the At the time But as they were the said some most FFTW has expressed of thought each point is from low centroid right of this also walk it ways out less having exactly this is why the wives quest some sort 40 needed for why interesting to know the city of some of squid and said it just takes over lad distances to the centre of the points and some of them up and which it ways all plans have borrowed you be interested in that What kind of quality the in truck plastic what exactly this cry to mesh because the head of the Red to use some of Where's the close on point and the club stop to and for the world to Britain's clusters of the Russo some of where the men were beverage distance of but the point is of a high the virtues of their point to stand for the world of the law cultlike so you might have a very very bins Kroldrup above them a lot of wildlife as around that will increase the city of up and so this is a perfect metaphor intracluster from well and actually this is exactly what we were 3 times as a quality mesh because the quality of the cost rate is basically the recent residual some most British each individual take left summing up will lead to a measure for the winning try out clusters of quality in the last precedent for all visitors to minimize as well you to keep the closest kind of 1 of my subcompact under the that means minimising the Everett with this which the point and it cost them
39:24
So let's go to the average became Elgar's what do we do Group need cake last so do is the randomly pick carried a point And just for the dead assuming their seats that in different classes and these will be the initial centroid each of the and each of decay Clusters world contained exactly 1 of the central in order to deal with the the other that points along nuptials get what we do is not abruptly discos for themselves them and will sign every point to the Costa where the distance between the clubs and put this point is minimal can't stop basically the I'd some to 4 points and while the kind of thing the centroids of classes covering the whole space and not might get a new point you and that was signed them to some of the cost by just measure and the distances to full of the central now will probably find this this 1 year Cimillo distance so this last will be and lodged to contain also this side who had taken a seats and and that will only points either side each of 1 to a closer with a close centre are Kent where by police point into some classes at the Central will shift for example the cost that we here now slightly bigger centroid is no longer the point or Italy chills but we have not after the but public yet again for the central would change and this is why we have to recompute Plessis based on the data points to find the a way to cheque whether the Sustrans could not and would not get to bed will just stop Oleg and and try to reclassify the points with the new central sold for example this centrality move delivered just take away ladette was new central as sentence of accreditation reassign allpoints again cheque but a cluster good enough could not means checking the city some of grant and and do it again actually easy if you know what good enough and that to face either the palms reassignment of the classes centroid has not changed much of it the if the classes centroids don't change you will get the same plastic because just distance measures that causes not not of a very sensible thing to do it computing the same Clustering times and I can't for the 5th only smell change you stop the you save and the stock world just make 10 8 races all 22 regimes had enough of Maxim number of iterations that you can't look at the city of some of Qwest and I find well but if you CEO solves Australia's 1st below certain milieu I'm totally satisfied look and and that will be it for me to sell the stake in examples a read take a couple of points of for example this want and this these are my classes central sun Mouw and stalwart assigning fullpoint and that to classes fittings these 2 points to the fact that a that was distance solve this point he is closer to the centre but that will be to the UK and that the 2 classes 1 0 idea and the 1 area where the 2 good Clustering Falmouth no don't 1
45:08
This seems rather smell the and this has a lot of Long Way in the city of some most quotas will not be too impressed so that will be expect in terms of a cluster of prose something around the lines of well this year looks for the complex way of this year something like that more intuitive can see King at the so take them that they are a sign of things and of Qwest with this centroids move house because it was so you for this classes and part of the of the of colour and part of his somewhere here in the middle of Adele and the centre of the somewhere he of this can be exactly quantifiably mood so of skills discussed and and we do not this we stop over again and reassigned where the point and the steps to the new central and when we do that after my iterations we end up with took just when you and 1 in which is pretty much what we thought we stop to must thing some point here and some point here of something and took that does not even tool strongly depend on the seat randomly to the might affect the a number of iterations that with the girls but if would have chosen to this point here and probably disappointing randomly at the start of the perfect elect would be immediately end up with the perfect Leicester but even these to remove child points to results after only 9 iterations in reasonable or care
47:40
Everyday understand what they mean testing the but have said that if we come to the movement of the centroids erased seek we are also might find that the way he was and what moved its smaller every to reach the Super 14 or more so in the beginning when you really take random appointed space you will probably estimated to be very far from the original central that after a couple of iteration to get close close and at some point is that noticeable of the scent changes and this happened because you redesigned 1 point or 2 points to different Clusters might change the centroid diluted the fact that after full of the mass of the cost is already a on these points and the way they like even if it is to move to that of the basic the and the advantage is that it's a bit relatively efficient for what you do you are in the order of the number of objects times number of Clusters times iterations and the 2 races as rather smell of the number of object to use your because of thousand millions of object but you tend 20 50 iteration
49:25
As Lambeth justice is Rollo rather small huge Sultan 20 year order but it's still the number of objects that he of pasta each from the determining fact much across the board but and work with the family but they are often terminated some local optimum well off of different lasting would not about make sense and model and 1 of the disadvantages is that the machine the centrally has to to be defined some useful way but the do if you have categories like a red green blew up middle distances and colours that 2 of them go some way to make it that easy to compute so categorically that is all the problems can really deal was found the next day to find the number of classes and wants it to a fight that will 5 Clusters even if 6 within better after But it does not noisy that and all applause and and a graceful fashion but just on to supply the and its unsuitable to discover cover non context Clusters so if you have clusters of that might have looked like this is a very bad thing for the game against the during the usually look rather roundish of a But the 1 of approaches cover of the December on the 1st of this came at a time basically him or if you don't use of the of the central assigning though objects of cluster but use a real existing debt object instead of dissent and you always use this that object was lying closest to the centre of the basic ideas and has seems is kind of like similar Jamie and what it does but uses Clustering so I'm if you if you find objects to Clusters the difference to the centre the distance to the central at the basic the determined the factory of higher much this is a pop the ball a member of the cost of care closely to all the more for membership in the club to get far off from away your less membership and those of so called malabaricus Clustering are where you basically you have a maximum likely classify and huge assuming that the best and generated randomly around some ceased to seek painful set point from the left to find the paper and that I'm most probably the seat for UK and so human just the other way around to generate the data from the Central point and then by looking at the data as an instance of a random experiment you may find out what although the correct point to those centroids as central of Fastest assign all points to the cost and the UK and also possible it works exactly the same way works executives algorithm where we were talking but this spring the took a rocket during the things we can do it for of so on that cost for of reconvened and that will have to hand over the lecture tools to realise all Casoli 22 who spoke Amantino of about the flat last 13pc will continue with him that he will cost of cost is has advantage that don't need to know that he had not need to know homing across stand with results so the 0 1 0 1 bomb at the less gave the buyout of him that he compressing as the name says is that the across the organised in a tree in the logic of this city's it presented in a in this case as the underground is what you can see she this is that of the frustrating of when you start your head the individual points and this individual points have was your distance when competitive and said of sold to simulate these were
54:52
Once you during the for example here and willing to join 1 intriguing across the and for the 1st level clusters of cost base distance goals the quality is bit of worst I'm the same thing she in this example where will join the board moulds full and 5 you can really see the difference in the game quality the squad he is worse than for the 1st joined and then you can during even further based on surround objective function which gives us the simulating hybrid during the result old with what they get from joining the stalled inside the numbers were not its number of 3 4 and 5 in these dendrogram tree and the and starting from up and pulled up the idea of the cost including golden all this is basically the idea of him chaotic across applying the bottom up of procedure this is also called the hydromel depressed because we have are something the bonds and well below the level by Labour lodgers Costas but the extent we merge the closest there for the cost us all to a lead of the spade condition can be he fight no became the number of processors want to reach for a can of costs will go up to the biggest Clusters on very mobile points
56:46
Of cost also Paul bound by the need to start with single terminal diagonals lost out with the whole of the origin of the world and say I'm going to perform speaks it's not exactly the same idea only not so why England with with all collection and and based on the the singularly the for implies unwilling to perform speeds of the state and they will stop when reach the single cost so which now use its own across this process is also step by step explained through the Rumsfeld Sorok and only points a walking the level in my team and I had with my across the American's and that's my resolve that start with that of a man at the cost the union discussed but about how the scandal polite and always so simple idea would be to create a 32 per cent across so across the represents of a couple and that the teenager initialisation
58:00
After and in some halted the site which points out on to large have said that will believes that quite so far this season and it took up create some kind of distance the objective function a discussed about also you know he means she eye can take it Penisini like my ex wife decide based on the same amount weeks which function will use for the simulated my pigswill talk later will talk about the then I'd do myself simulating thinks for the point ahead right now and then Heinrich the points for single cost us as a with have the initial step would the maximum seem or this most distance between them the 2 1 game and open space And if I'm not trading so if the about 9 miles from much more Clusters bemoaned the cycle to abuse sold by finished with their initial step unwilling to Google and Computing again this simulating my pigs did the points with a high simulating most them and goal of cost the biggest problem is highly will conclude approximately between from what measures so there was flawed approximate will discussable based road specific for the full year to see how it works on example late imagined that will this points out of a single can cost sold in each of these circled the has just won a lemon as a set of wheels with a new idea came out
59:54
But this is actually symmetrical megapixel not actually and not interested in what steps I'm actually interested only in the of of the simulated you might think and then have just 0 compared the 1 with people here because the result of the simulated between these before then another 1 and another 1 answer was search in this metrics the their with the highest realise or maybe she added This is a distance Maddox with the most of the smallest and will decide that the vacancy or at this level will join the SPL for example because I've seen between them there is a high simulator would pretty pretty easy to get you But below the point in time we will be reached with city of big across the sole for example a 1 across the Shia another 1 here and so on in the UK also drawn into dental then programme the simulated emetics also reflect this sort each of discussed those is actually said over a laments we did combined and do this moment in by time now what happens if 1 for example Most display adamant so iFund from my met that this book clusters most senior will actually if I'd want to afflict based in the programme and that it simple and will be joined them in the tree and then operate more it's not that difficult and this is how always a walking and John joined useful these 2 Clusters but public duty the simulated picks up United before they did so well since the ideal with sets and only going to provide a of the points on game by bought subset to the England side to say or came ahead of a big said here and the biggest here and I'd just now want to wake up to the scheme from the idea need of a complicated because right now where much and the order of the medics has been news because in national them out by 1 and a tiny now Book of made it simulating on the board of the cost The big question she what they were the beginning to the end of simulating measures can be used in such high on the clothes were pasta and this is actually go and open to a staple of this kind of cost during the road in the literature Mamie solutions for something like this but he is also the name of the odd 1 out of the room at the last and 1 of them is the link when and willing to to use the name of the stands although closest points Oftel cost for example of different were installed the family animation she yet
1:03:26
If unwilling to apply singling as a Simulated idea the closest and and useful Clusters their closest points by measured the you began distance between them and say that housing in the SPL Clusters are quite easy the opposite the computing Clustering ICI well another solution would be that to think the of the most current the about points from this will cost NHS each of the multi Joba the procedures has it Vantage's and is advantages we discussed above the enjoyed the and the best solution would be to update over the distances between the points in the cost of this would be a group of each Clustering simulating and is your public view you have been treated since the way they have Priority have centroids could just how good the distance between them its discussed but the both the told them so this including across the if I'm going to take the closest lemon is my simulating domestics then I'd Campo deuce may change of Costas Mitzi would for example and this like classical solution for across the city Fry would use the simulated functioned then would see walking that the distance between those who also they should be and together by should probably used the same using the same simulate there should and also during the book so it will result in such a line across the usually 1 put behind to avoid this kind of want to use because it has either said we want to increase the university life but if you look at with these not and that these mould the right of the party so interested but it is quite bad this is why when you have you independent purpose but when you have such a big of then you might want to avoid the same moving simulating although it is very simple popular the solution but complete Majid stimulating the when you think the extreme points well what had wanted to have some up where which can actually have 1 critic of the City this year to stay here and then and you have a song with a completely gives simulating your compared this point with this point sold this kind over over metric has a big problems with a new deal with outliers what came and blamed in such cases where he was
1:06:45
No complicated 0 may take the proper every frustrating where public over the distance in a big and accompanied the distance for the points in the opposite cost but and the same for this 1 but this is competition expanse of cost this would be the ideal place but you have to imagine that in the case of a powerhouses both of these regions will have built up points sole although the best solution is not easy to to do something like this is you have included The last shouldn't used fulcrum compared with the position of the centres of the pool of the book Clusters which actually is very used when 1 has to do with the other players in the daytime and when 1 has to to avoid this including the phenomenon of the team's only when you do something like this in the UK alone and that the case had this grounds these street and you start with a pay off the said there is your single Bono and when when no using this century across the menu manual and up with over next in 3 sold the tree would be autumn is not the be problem but still this is the classical problem when he was sent across the in this was for the good of the collective Clustering but spoken about also the other solution where you start with whom the bond with the because the and then you make your way
1:08:34
Bound It was more Clusters the idea behind this is that you can actually use the that lasted Inglewood moves broken in the 1st part of a lecture so you start to the because the and you say I'm reuse gains to speak and take into the simulation but I'm doing binaries sold had will told names and they get to London seeds accompanied the corresponding Clusters and then I'd go recursively in each of the 2 cost so that the basic basic ideals devices custody of players the heuristics with Stepan about the game means of their 1st year or so of the pulled to be used for a very small Costas This is and the case of generating about life he maintained it you would have cost that all of 1 and a man you would have generated something going here some made and and in New York have here some and probably this adamant she which is to be an outlier will be seemed like a means and games with that he said influenced by If you don't know if you lost smoke speak was and the and the this is number of documents with the but it is more cost of the fuel something like this that you are at the very sensitive against all classes if you cut out where salt Benicassim OK actually have to cost the city this 1 and it was this 1 This is the 2nd of stake which says which should over a plan to split the 2 extremely different had been these so we should all always keep in mind when we all thought the possibility that the Cabinet is over resulting for subclass should be balanced Could this brings me to a below the Dome city will be totally looking spoken up about up Where's saw what is on offer by public explained herself publishes outlet for 1 student me what he on the stands on the victim of don't eat
1:11:17
Nobody wants to know what he wants to get rid means that the public so actually it's quite simple but or you have large collections of have distribution so they buy a new car this distributions you can see datapoints rejection don't fit into the team each usually this distribution approach can order this you will need points than the stick together but brought about this 1 what the ball based on what the ball but when you could say it's all quite its stake itself out Lessing's that only just 1 point the imagined that you would have here The P The point is this then and all wildlife or not in Pentagon defining semantically and then you can say Well what actually differentiates pretty much bigger muscled they found itself I'm going to be defining the amount of light so you can also established that threshold for it but usually and that the perception in the night but the defensive every night the between the said of the top which is outlet and and Distributed they found is quite high processors over all clients have recently seen a presentation from from someone who has done he is of people not by sold his stood on the books by the end and he was investigating the quality of the data he has seen that it is the issue of global pop and then he had to investigate whether the felt like something and he has also observed that most of the players come from their own problems from his communications between different components of soft so won his 1st if the all players come from but they don't care if the data is actually quite a bit and there was an explanation for 1 Classico example is when you go about your often with select and I want to see of the salary off your employees compared to the beach
1:13:49
You can see that walking this is quite normally when they of this is quite normal here it was into this division but when you look you see someone we choose actually about 10 20 she earns fuel no small salary What is the reason for well maybe he's younger have the will and the investing is he according to wildlife is still problem which are Baitullah not actually 1 of the show that this is a part time job or a student you could find public semantic of explanation for what you want us to sell about something like this where the pages usually under 20 of overs somewhere of about then also insurance about closer wanted thousand dollars a year this could be a problem in they will you could of the sort of something like this again maybe semantics communication this might be to your son so you can be fined up but that is coming from for those losses 1 is your bed the Dow 1 is some semantical explanations Well if you get The investigated aiming to established that this coming from the date that the question ballooning although as you have problems with their across he also have some useless information about case that my manager But as this kind of full of money and you might also get some surprising interesting information students might be a good idea for mapping job of cost around the because of applications are players
1:15:51
Public fiction For credit card for example Or even a little bit fiction renewed comes to being there were river on an interesting optical recently with a man called received a bit about this high told this is a clear all just stupid to would have achieved the job in no stopping such a beautiful and going to the man sold Such such things can be easily evaluated by the scenes where speaking the ball but the where House's white people need to be picked up by the band for a 1st run of the nice but it is when you find the semantically explainable life you find a statement Will you called the based so it could lead to some which approaches and the very good example for these this has actually have happened is the sort of policy insurance cold and the idea here is that if they wanted to perform Clustering and customer segmentation so they've done and a civil clothing and including PPL classified my ex invented by mobile medium and high and have different types of crops trucks vans sedans transport and they have observed and apply in the case of people with a mental accidentally will be the drive spelt out and then they said wrote this is changed it was usually the exit interest rate for the owners of the house is pretty high and then rest the gated this this segment and say what the reason the good though some members of the people of beauty multiple of his incidents Whitehead and then we have established that there is actually a group of 4 drivers soloing mid for these with the kids with high income for the 2nd part of the car which is a sports car but they drive for the killer of the their left the responsible for than they OK white and to profit from it and I said if you have to keep going to the Moon and you have a high income and the 30 0 2nd order the will be a much Murphy's even if it does bought because they were so such people close to the middle accidentally so this kind of public are not since which is semantically explainable has helped them become exited the favourite for for his side to such a sensible earnings per OK publicly detect buyers of cost the row of different the matter a where interesting about not what we can see with off eyes you can profoundly almost small amount of the public want to believe you have a large amount of data sold 1 solution would be to perform across the banks which are not sensitive ballplayers and in search for points which don't like in any cost another solution would be to set up plants which became very differently from the not so heavily distribution and in search of the points which have a high rise
1:19:33
Of course you can use statistical approaches You can measure distances order relations with the include and so the classical and the most poems easy statistical distribution need to have something like this you can do is do the to see model and say Well everything about the seem overseas succeed is an outlet for me it's about socially from your bill that would and the funeral that this is the best motioner off your data the other solution is used distance based approach about the euro but only part the day but distribution
1:20:20
But of in this case you need to cut the journey but sold to say so who need to establish what neighbors each point has and the of cost you can do this based on the distance with all of the points from self and then the finding that the soul of the city and the world who has just won a plausible you can be funny as an nightlife notes which have father modes around them and it is you can imagine something like this Of course I'm going to take it to the simulation with and cooperating with a ride using a multidimensional space by finally were sold this is an if the the same thing here with same rather find the find the a but it's not so pretty easy of their cycle for the family is also 1 of Asian business about in the case when they can described the lemons which should be the so and the description and then they can't copies based on of this description of man seeking to cope with the deviation and the women's with high division of them my all plants are found no a which simulates the human behaviour of distinguishing those IEEE usually take saw something which doesn't think imbued is the focal Brussels sequentially exceptions techniques can find more innovative but the role of the state yet again his below the powerhouses the we are applied and this is exactly what see in only lanai medical code persistent that the back of the Hewitt a where interesting in finding on and not the right now interested the city's semantically of the is in high multidimensional data the solution for funding such such it is discovered in the even so I'm going to go from 1 exploitation and that the idea is that a need to cut the myself unexpected video and people know what I'm going to expect the and discounts to remember in the early hours and we are dealing with 2 and a book of is expected to sweep from this also statistically with Auckland for pictures of the bulk of the book about aggression on highly says we can use them usually using addition 1 copulated expected various or not films and then I just thought they'd the deviation with each day we actually have in the queue with the expected and this way of cost again scene or 66 the model will give me they all players in and out to them will including the last part of following a church was spoken 1990 about 0 simpler cases of both last week before a poll for their mention of may be threedimensional frustrating but the lower said that the beginning of 48 turned into a powerhouse whose we deal with high dimension a so we have also to speak about how we should live by frustrating when we have to deal with high dimensional data about 1 in 4 were referring to the large because soul of cost this is a big problem she then about what it's like finance is 1 would not have put it it's based on became a delayed so you remember he means came made very it is the 1 where the centrally sections of jigsaw nearest the sent the ball Dignitas dissent and then the another Russell sooner would Roach stream and cost in 3 high command of mostly non for something like this the advantage for this kind of system in a way this is the have pulled for of the problem in the complexity tool just 1 possible would be that have been made in the past and a as it team just imagine bespoke stop might have for something like the as it stands the standing of what it has to cost the state now so it doesn't have the advantage of doing something like Beckham or she across the of the most used techniques when we have to deal with high diamond Chanel team is to be used IRA of undermentioned so they mention of the Dutch if you have a lot of the endangered lucky in Canada show problems with just poultry dimensions of hard work by mentions I'm not correlated then you have a problem or a of costs another problem use when you measure this was due to be a distance in the stands but it can become a hunger and what the skinny Blue is the think about cost which are not have been damages and I want to find the custard and printed a mention of the time you can actually find the cost the Subspace the standard a mention of space public how find the seats the cost by mentioned in the 16th dimensions base
1:26:26
If you would like printed amateurs you have no cost he spoke to 16 going with 16 by mention cost so that actually a problem when Clusters exist but not to the highest dimension of the given the Andrew Speaker a booking for the Classical netbooks for dealing with high dimensional they talk about is said to have been mentioned in dimension of the reduction to a singular evaded the composition of this is that classical away when a can decide which which of this dimensions actually available
1:27:02
If the dimensions which actually can be used with the composition Vandyke and cut them into a IBM and had a bold and I don't think Prince Michael stating that much so why with book reduction I'm actually getting rid of corrugated and the band the something like this and the other is the said Subspace costing want to identify the cost that which is not just in the highest a mention of the date the but it's across that in the middle of the mansion 16 is across the 20th is not cost the classical are worried that this is the man who will discuss about this are worried for the So the idea that he is wanted by the space which is the amount which has the highest their mention of the word and eye can still find the cost full of cost may be into the the 1st day mentioned it would be easy to be a case where there is a Clustering is the 1st time inch if I'd go into the 2nd leg mention it more complicated than it based aviation Whitehaven the 2nd dimension across the man going to the 3rd and so on so you can only built this is kind of when he got the cross you start from the back into a tie for their way up to the band mansions and you and you can find the cost of the new NEMO and say OK if I'll go up don't have across England where can stop in this dimensions and the and the idea she is built by patient each day mentioned including the about his like but such so you have to have the example of the laminations then you make this kind of about mention of My their mansions out of costs by didn't mention for example here the to to but I'm going to walk said it might be the point But they were not if over a profound this party the shooting of the space we can speak about you need You can imagine this 1 he said is a onedimensional and you can imagine the days when he is for their mention of pretty pretty simple and then we can speak about density units of based unit is a unique in which objects of about this opened Ahmad solidify would imagine that ahead have of this bomb that it is JLN and then commonly based used where them in 2nd implemented you see the green Hominine this your knees where she indiscreet Citigate he wanted to tell me what this year This exceeds the number of objects given as provided a simple imagined that if you had a few points offer points that dance unique we like the ancient because the shuttle are Kawano doing this space is now he should take the example of this 1 is not the because it has just 1 or 2 that are also in the tune of cause them of their instincts were unwilling to speak about this disunity meaning cliques or a busy member of the Bay of about was certain threshold it's easy then would want to speak connected and should you have to imagine connected units mitzvahed big easy case for their mention Load space for this do this units which an age for this Programming another will define willing include do is a transition saw for example if you have another this unique here at the end you have transitioned this 1 he you have visionary 1 issue food and just before you somewhere she which areas with local disbands units and this is a transition a unique include this transition unit Ikon decide that the 1 in the pool connected density OK so I've just despite defined the simple relation of connected version the shirt and edge over the transition in this position in doesn't have to be dance or so it can be an empty Bucking the Oslo well for that The principle of the use of or based on the popular with with my probably in the 1st picture of the company's the idea is that if you start from a low of their mention of space each or go upward into their mansions and you want to know and I want to be there mentioned units the kind the band's is 1 of the league's building new when joining the unit's doesn't belonged to the about this units From the very Subspace this month's that I'd can only get carried a mention of this units by building from the beach and its from the key minus 1 Subspace Way I'd don't have the will of all the combinations it can only be came minus 1 and during what they have in this it is units Had a mention of events units OKC discussed it would make it will be good if we discuss based on example the major step including ninki is identifying the subspaces the on the cost us we want to stop somewhere we stand on the 1st level and we want to stop somewhere where they can identify Clusters any more found so that would proceed them available was dealt with 1 by mention substage Subspace and then would be higher by mention of subspecies this means there will be next the 2nd of their mentioned Subspace and and the the onslaught of the former Louise my work and according to the popularity of the idea only the commonest minus 1 mansion things you need to be the next high by mansion so this is not need all the Tree adjusted the of minus 1 diamond should forget late seeing an example We will take 1 by mentioned Space looks crossing the age the 1st access and that's costing the anything about for about adamant is there should be the 1st unit is this 1 and the 2nd is this 1 and so on is this between 20 and 30 but this unique where has Tree elements in a hell of a slight have each year and that 1 of the nation's between 30 and 35 by here of these boys lemon sole wise to him from the race big the density vomica between 35 and found head 5 again it's OK between 40 and 45 ahead to eliminate is great but have just 1 of them and you have just won a and them band should identity to use them and for the combination because it is with previously said they want the 2 biggest unions and highest basis IBM mention of spaces saw the she would like any more for on makes coccyx handling the same on the edge of so this is my 1st unique young 1 management unit is nothing that so it's not that this is clearly bands with the mood of this is good and so is this 1 she this is also and this is also the sole not why have the onedimensional low bands units and then pulled on buying them open higher than mention of the incident this is rather than she saw combined and opened for of combinations of cost but afterwards and actually the and see what from the new candidates the combinations of the onedimensional this unique is actually also eventually according to the data what they performed by the now is on my structure for my information about the data but they did not take the bait so now we have perform actual about their mention of cutting process with a team mate of the notes which are not mentioned now and I've said OK will combine 20 per day with three 20 the and between is said she this is not the so it plan to be between the tools and the ingenuity of high level l by will go further and combined 20 30 with more than 1 with 5 6 this year this is also a candidate which don't need now this candidate has been introduced and and the nation's with this 1 with 5 6 which that by 1 dimension of level was eventually because look but not the cause of this 1 now count sided with big Gengbin that data to eliminate dismal 5 take the data I've seen that there in the for their mention of space dismal bands unit flag and taking the people and the United is out and they were further and combine case where boundaries and the and the combined with 6 7 and taking this is dense unit its this multi its eventually sold this 1 will be in my reside in this is the 1st sickened by mansions Branch and Bound mesotidal combination after taking the right foot of candidates take the and out on 9 all have 1 she will will continue with a combination several days but the 35 with between the state and College this 1 in this 1 and so on and Baghdad the result of this by the instincts
1:39:23
For the 2nd damage on it sexually these points yet of cost being an easy example you can see it with your own eyes but the idea is to have a local paper and you have a high by mentioned it would be difficult to pull imagined and identified Stansted you can identify them how ever by doing this the 1st step from the OK so they have a secondary mentioned bands units which is actually how much were going to go on this example of the 2nd step is identifying the Costas I've not to identify units but public establish what my Clusters is in this that becomes worth received the unique sold disabled events units which have established TV asleep and it tries to boost is the politicians this said on Blue Osipova subplot patients will each a filaments in each of these applications high on the list spoken about what the team is a band Shumeet's by the shooting of an inch or 2 of the times I unit which this with local their money but to if to remember is the connected to social the discussable a community had not been this low this book at the end of their own How can find the resolution from this well it's a bit of each other a classical for this idea to start with a seat in the City is that on the high road in the city of Linz units be this 1 and then Hyogo iSearch around the globe connected mentioning of cost the solution she is justice 1 2 0 transition this is my might has TV puny this is not the answer but chose the said she and research with sold this unique here condition each year out of and Quinn a young it goes from the publisher And this is where stop because account go anywhere more refined and lemon from the city which in these events unit connected with disarrange the next and she Democratic now strike on quality for so that I'd find my 1st said 1 and ways to see head of the party should all because part the shouldn't lemons so idea that on them by the end when or minus 1 c this 1 idea that on the 1 side from she ran into the problem and British European saw the same thing and and a bigger where across OK with a chemical what might have connected on me and the only then see dance unit that the have is this 1 on the right so that might be it and I'm finished with you so ahead of this population of events in which are connected inside the case sought after the families of the 4 subset of 1 potentially give me the ball Clusters the the politicians say for killing you have across the Shia and you have across the she but that would still need is to describe discussed with no have gained about this this units which interested in the way of world but don't know how the cost groups like so that the state would be a boon for the pulled the SPL subset which operated for their mention of anti tried to intimidate the description of the corresponding PPL cost the programme has been would be treated intimated detection here in the hot problem because the and trying to pull the Oakwood each with the mean number off regions of Maximo rectangles which over these days you need to soak as the best solution for this kind of problems that could use it is agreed the outward which is able behind this kind of quality and the idea is to stop achieved with each of these politicians you start with the 1st politicians for example we've identified a given that the current get that on the scene midstate will start here and then tried to grow enjoyment trick Dando so this is the joy middle the in over the benedictions based my dimension a good saying will in the world except Cecil for but age unwilling to the left and then to the right if unwilling take extend Matekitonga rejects started with from seat and this is just 1 account expanded to the left because berries Obinchu need to the left so there was no reason to expand over which it cost in an area which is actually a again on expanded to the right and then
1:45:39
I've costs have to look also only other Behrajan based on the 2nd from their mentioned upwards and all wants to see if iconic extended Saunders the by erections and music and securities and the Hughes and so icon so the much simulating local worrying might this might my 1st step is this 1 the Dome is a question if I've managed to cover the spot the shin completely and they see that it is actually 1 of unit which has not been public if it would have been met by the state would have been repeat that will generate and other runtime Sido but these days you need that's my 1st geometry my 1st direct and the and then try to get everything out of the benedictions based on my bed mentions accountability right now in where because there is no more than to make it was a pretty simple example so what they actually obtained these disjointed to here the composition of full back and by this I don't think for a 2nd politicians for this when he faced stopped at the he this 1 she returned to it on a possible that elections on all of them mentions have only by mention sole use of the for possible by excellence in the only goal to both is 1 and this is the during which gives me the description of the cost this is for their mention of case of cost the city is available causal for by mentions you just use a minimum bonding picked and Load you use Hypercubes and saw the vintage the use it or not the defence subspaces it stops somewhere in sizzle Kate this is the highest dimensions a confined cost for if you go any further although we would be the highest number mentions that I'm not going to cross the line up from IBM during this case is about the data distribution 1990 think all the day division I'd just started by affirming some kind of a way of identifying the this units are also road Vantage's of I'm going to try to test the communication possible Yesawich agreed disillusioned its case has been reviewed with the size of the implications can imagine that police used to could be part of being such a simple matter of the doesn't go used the word that best discussed cost so because they need the because it's all is greed is not the best solution I do good enough solutions and will relieve the handles of the lecture on just is a review with discussed with the about supply unsupervised learning we of stopped with King's Cross that in which is a permit for for performance but that Clustering sold before a frustrating just announced that the the solution is chaotic across that England where you start either bond sold devising or more them up of Rome the your starting for Environment Clusters was discussed about the or about establishing the figures indicate that there is indeed a which can influence the cost this is all play 1 other says you Schwable response to about the quality of should be before to using the costing strategy and we discussed the bulk of the solution in custody their mention of the name which allows me tool to find some species and subspaces when the horror of their mention doesn't providing across the next victualler the and the dollar housing so this was so exposed for the mining will group called this season so the systems will speak about simulation among getting customer relationship Management said cost management and logistics
00:00
Point (geometry)
Algorithm
Confidence interval
State of matter
Decision theory
Multiplication sign
Characteristic polynomial
Distribution (mathematics)
MIDI
Gene cluster
Hierarchy
Virtual machine
Insertion loss
Mereology
Rule of inference
Attribute grammar
Wave packet
Number
Independence (probability theory)
Pointer (computer programming)
Term (mathematics)
Vector space
Data mining
Information
Social class
Mobile Web
Algorithm
Spacetime
Decision theory
Cellular automaton
Bit
Line (geometry)
Multilateration
Local Group
Virtual machine
Entropy
Maxima and minima
Arithmetic mean
Supervised learning
Computer animation
Network topology
Object (grammar)
Hyperplane
02:14
Curvature
Social class
Unsupervised learning
Computer animation
Outlier
Uniform resource name
Flash memory
Data mining
Mathematical analysis
Addressing mode
Units of measurement
Maxima and minima
05:54
Slide rule
Coccinellidae
Ferry Corsten
Variety (linguistics)
Mathematical singularity
Simultaneous localization and mapping
Mathematical analysis
Shape (magazine)
Mereology
Emulation
Number
Social class
Unsupervised learning
Causality
Term (mathematics)
Object (grammar)
Process (computing)
Data structure
Addressing mode
Group theory
Measurement
Digital photography
Computer animation
Graph coloring
Lattice (order)
Computer network
Object (grammar)
Identical particles
Data structure
Window
07:43
Pattern recognition
Numbering scheme
Group action
Musical ensemble
Flock (web browser)
Service (economics)
Observational study
Divisor
Multiplication sign
Gene cluster
Realtime operating system
Mathematical analysis
Mereology
Scalability
Attribute grammar
Subset
Number
Web 2.0
Information retrieval
Sign (mathematics)
Term (mathematics)
Computerassisted translation
Subtraction
Position operator
Social class
Physical system
Pattern recognition
Information
Numbering scheme
Image processing
Morley's categoricity theorem
Data analysis
Bit
Ring (mathematics)
Local Group
Flow separation
Spring (hydrology)
Computer animation
Graph coloring
Hausdorff dimension
Order (biology)
Right angle
Game theory
Identical particles
Data type
Form (programming)
12:11
Subset
Cluster sampling
Measurement
Computer animation
Attribute grammar
Mathematical analysis
Information
Normal (geometry)
Emulation
Similarity (geometry)
15:40
Cluster sampling
Algorithm
State of matter
INTEGRAL
Multiplication sign
Gene cluster
Water vapor
Mathematical analysis
Shape (magazine)
Mereology
Semantics (computer science)
Video game
Type theory
String (computer science)
Reduction of order
output
Social class
Algorithm
Process (computing)
Spacetime
Military base
Physical law
Dimensional analysis
Attribute grammar
Parameter (computer programming)
Bit
Staff (military)
Rectangle
Scalability
Measurement
Shape (magazine)
Local Group
Subject indexing
Computer animation
Noise
Quicksort
Object (grammar)
Domain name
19:25
Axiom of choice
Cluster sampling
Metropolitan area network
Axiom of choice
Gene cluster
Emulation
Number
Attribute grammar
Measurement
Number
Computer animation
Hypermedia
Uniform resource name
Right angle
Object (grammar)
Social class
23:41
Cluster sampling
State of matter
Code
Distribution (mathematics)
Gene cluster
Emulation
Number
Heegaard splitting
Object (grammar)
Singleprecision floatingpoint format
Statement (computer science)
Abstraction
Subtraction
Social class
Data type
Distribution (mathematics)
Functional (mathematics)
Zielfunktion
Partition (number theory)
Hierarchy
Number
Computer animation
Uniform resource name
Order (biology)
Energy level
Object (grammar)
Data type
Thermal conductivity
Form (programming)
Task (computing)
Cloning
27:14
Cluster sampling
Musical ensemble
Context awareness
Computer file
Multiplication sign
Gene cluster
Mereology
Distance
Computer
Number
Measurement
Internetworking
Data structure
Subtraction
Social class
Algorithm
Exponentiation
Expert system
Functional (mathematics)
Measurement
Local Group
Distance
Similarity (geometry)
Cache (computing)
Computer animation
Personal digital assistant
Object (grammar)
Separation axiom
29:21
Cluster sampling
Stirling number
Number
Computer animation
Element (mathematics)
Heuristic
Physical law
Exponential function
32:10
Point (geometry)
Cluster sampling
Tuple
Perfect group
Algorithm
Connectivity (graph theory)
Multiplication sign
Gene cluster
Distance
Disk readandwrite head
Emulation
Number
Attribute grammar
Summation
Residual (numerical analysis)
Bit rate
Average
Object (grammar)
Physical law
Social class
Physical system
Area
Curvature
Polygon mesh
Cellular automaton
Closed set
Point (geometry)
Physical law
Planning
Vector graphics
Measurement
Distance
Maxima and minima
Partition (number theory)
Number
Computer animation
Gravitation
Right angle
Object (grammar)
Quicksort
Units of measurement
Square number
38:40
Point (geometry)
Cluster sampling
Maxima and minima
Algorithm
Multiplication sign
Mathematical singularity
Gene cluster
Distance
Emulation
Number
Measurement
Maxima and minima
Goodness of fit
Centralizer and normalizer
Mathematics
Subtraction
5 (number)
Social class
Area
Spacetime
Point (geometry)
Fitness function
Measurement
Local Group
Computer animation
Order (biology)
Iteration
Reading (process)
42:58
Number
Computer animation
Iteration
Mathematics
Set (mathematics)
Thresholding (image processing)
Maxima and minima
Wide area network
44:25
Point (geometry)
Sign (mathematics)
Computer animation
Term (mathematics)
Iteration
Line (geometry)
Mereology
Resultant
Social class
Number
45:50
Cluster sampling
Context awareness
Multiplication sign
Scientific modelling
Disk readandwrite head
Data model
Maxima and minima
Mathematics
Object (grammar)
Convex set
Social class
Covering space
Spacetime
Outlier
Point (geometry)
Open source
Bit
Instance (computer science)
Maxima and minima
Category of being
Graph coloring
Network topology
Uniform resource name
Order (biology)
Whiteboard
Resultant
Point (geometry)
Random number generation
Gene cluster
Virtual machine
Similarity (geometry)
Mass
Distance
Emulation
Number
Centralizer and normalizer
Iteration
Subtraction
Inheritance (objectoriented programming)
Element (mathematics)
Set (mathematics)
Number
Spring (hydrology)
Computer animation
Logic
Personal digital assistant
Factory (trading post)
Iteration
Object (grammar)
Game theory
Family
53:27
Point (geometry)
Cluster sampling
Greatest element
Inheritance (objectoriented programming)
State of matter
Diagonal
Gene cluster
Distance
Coprocessor
Emulation
Number
Sequence
CAN bus
Labour Party (Malta)
Singleprecision floatingpoint format
Network topology
Energy level
Extension (kinesiology)
Subtraction
Mathematical optimization
Metropolitan area network
Condition number
Process (computing)
Bit
Greatest element
Radical (chemistry)
Computer animation
Network topology
Hybrid computer
Game theory
Whiteboard
Procedural programming
Diagram
Resolvent formalism
56:46
Point (geometry)
Cluster sampling
Algorithm
Gene cluster
Knot
Distance
Open set
Emulation
Measurement
Maxima and minima
Latent heat
Military operation
Singleprecision floatingpoint format
Moving average
Mathematical optimization
Metropolitan area network
Spacetime
Computer
Point (geometry)
Set (mathematics)
Distance
Similarity (geometry)
Computer animation
Heegaard splitting
Website
Key (cryptography)
Game theory
Cycle (graph theory)
Matrix (mathematics)
59:31
Cluster sampling
Pixel
Multiplication sign
View (database)
Outlier
Complete metric space
Subset
Summation
Pointer (computer programming)
Video game
Linker (computing)
Electronic visual display
Link (knot theory)
Point (geometry)
Moment (mathematics)
Measurement
Distance
Metric tensor
Maxima and minima
Computer simulation
Network topology
Uniform resource name
Order (biology)
Right angle
Whiteboard
Quicksort
Procedural programming
Resultant
Point (geometry)
Numbering scheme
Gene cluster
Average
Distance
Emulation
Local Group
Ideal (ethics)
Energy level
Multiplication
Computer
Set (mathematics)
Line (geometry)
Local Group
Similarity (geometry)
Computer animation
Personal digital assistant
Universe (mathematics)
Element (mathematics)
Game theory
Family
Matrix (mathematics)
1:04:32
Point (geometry)
Cluster sampling
Algorithm
Constraint (mathematics)
Gene cluster
Student's ttest
Average
Distance
Mereology
Number
Local Group
Chain
Video game
Ideal (ethics)
Position operator
Metropolitan area network
Social class
Dialect
Simulation
Link (knot theory)
Inheritance (objectoriented programming)
Outlier
Binary code
Planning
Thermal expansion
Similarity (geometry)
Maxima and minima
Singleprecision floatingpoint format
Arithmetic mean
Computer animation
Network topology
Personal digital assistant
Uniform resource name
Heegaard splitting
Game theory
1:10:41
Point (geometry)
Web page
Presentation of a group
Connectivity (graph theory)
Multiplication sign
Distribution (mathematics)
Insertion loss
Mathematical analysis
Client (computing)
Student's ttest
Mereology
Thresholding (image processing)
Coprocessor
Semantics (computer science)
Object (grammar)
Category of being
Subtraction
Source code
Information
Outlier
Statistical dispersion
Fitness function
Bit
Division (mathematics)
Cartesian coordinate system
Maxima and minima
Arithmetic mean
Process (computing)
Computer animation
Database
Personal digital assistant
Telecommunication
Quicksort
1:15:42
Point (geometry)
Sensitivity analysis
Musical ensemble
Ferry Corsten
Distribution (mathematics)
Gene cluster
Mathematical analysis
Bit rate
Mereology
Discrete element method
Device driver
Video game
Bit rate
Telecommunication
Dedekind cut
Smart card
Subtraction
Metropolitan area network
Family
Metropolitan area network
Rule of inference
MIDI
Multiplication
Outlier
Bit
Incidence algebra
Local Group
Demoscene
Maxima and minima
User profile
Process (computing)
Error message
Computer animation
Personal digital assistant
Smart card
Device driver
Order (biology)
Statement (computer science)
Quicksort
Data type
1:18:38
Cluster sampling
Standard deviation
Outlier
View (database)
Set (mathematics)
Distribution (mathematics)
Point (geometry)
Distribution (mathematics)
Dimensional analysis
Attribute grammar
Distance
Mereology
Measurement
Distance
Emulation
Order theory
Data model
Probability distribution
Type theory
Computer animation
Personal digital assistant
Statistics
Software testing
Normal (geometry)
1:20:07
NPhard
Standard deviation
Complex (psychology)
Linear regression
State of matter
Multiplication sign
Scientific modelling
Sheaf (mathematics)
Price index
Mereology
Data model
Cube
Object (grammar)
Videoconferencing
Physical law
System identification
Multiplication
Descriptive statistics
Rhombus
Exception handling
Physical system
Software bug
Spacetime
Outlier
Flash memory
Demoscene
Distance
Discounts and allowances
Hausdorff dimension
Right angle
Cycle (graph theory)
Asynchronous Transfer Mode
Point (geometry)
Sweep line algorithm
Algorithm
Cellular automaton
Mathematical analysis
Streaming media
Distance
Sequence
Measurement
Data mining
Queue (abstract data type)
Loop (music)
Metropolitan area network
Rule of inference
Addition
Standard deviation
Distribution (mathematics)
Characteristic polynomial
Division (mathematics)
Computer animation
Personal digital assistant
Revision control
Linear subspace
Exception handling
Family
Form (programming)
Series (mathematics)
1:23:34
Metropolitan area network
Cluster sampling
Random number
Netbook
View (database)
Set (mathematics)
Mountain pass
Gene cluster
Dimensional analysis
Distance
Measurement
Computer animation
Hausdorff dimension
Reduction of order
Hausdorff dimension
Sampling (music)
1:26:41
Cluster sampling
Rectangle
Group action
Clique problem
Equals sign
Disk readandwrite head
Data model
Hausdorff dimension
Physical law
System identification
Descriptive statistics
Exception handling
Metropolitan area network
NPhard
Spacetime
Theory of relativity
Building
Structural load
Basis (linear algebra)
Electronic mailing list
Parameter (computer programming)
Bit
Demoscene
Flow separation
Partition (number theory)
Arithmetic mean
Uniform resource name
Simulation
Point (geometry)
Algorithm
Branch (computer science)
Axonometric projection
Thresholding (image processing)
Event horizon
Number
Energy level
Boundary value problem
Data structure
Units of measurement
Metropolitan area network
Information
Length
Counting
Rectangle
Local Group
Population density
Word
Personal digital assistant
Function (mathematics)
Linear subspace
Family
Spacetime
Musical ensemble
State of matter
Multiplication sign
Mathematical singularity
Combinational logic
Mereology
Total S.A.
Subset
Image resolution
Programmer (hardware)
Pattern language
Moving average
Flag
Position operator
Rhombus
Area
Process (computing)
Point (geometry)
Twodimensional space
Element (mathematics)
Connected space
Shooting method
Fraction (mathematics)
Network topology
Hausdorff dimension
Right angle
Energy level
Clique problem
PRINCE2
Identical particles
Data structure
Resultant
Thomas Bayes
Frame problem
Computer programming
Set (mathematics)
Gene cluster
Emulation
Revision control
Linear subspace
Population density
Causality
Reduction of order
output
Tunis
Condition number
Multiplication
Dialect
Sine
Element (mathematics)
Bound state
Dimensional analysis
Incidence algebra
Transformation (genetics)
Number
Computer animation
Lie group
Customer relationship management
Object (grammar)
Units of measurement
Local ring
1:44:36
Cluster sampling
Rectangle
Musical ensemble
Clique problem
State of matter
Logistic distribution
Mereology
Order (biology)
Strategy game
Information security
Descriptive statistics
Physical system
Metropolitan area network
Simulation
Structural load
Control flow
Unsupervised learning
Data mining
Hausdorff dimension
Telecommunication
Species
Figurate number
Algorithm
Computergenerated imagery
Distribution (mathematics)
Geometry
Gene cluster
Computer icon
Emulation
Number
Canonical ensemble
output
Units of measurement
Run time (program lifecycle phase)
Scale (map)
Vulnerability (computing)
Direction (geometry)
Distribution (mathematics)
Dimensional analysis
Division (mathematics)
Line (geometry)
Scalability
Population density
Number
Word
Computer animation
Integrated development environment
Personal digital assistant
Customer relationship management
Dependent and independent variables
Linear subspace
1:48:56
Clique problem
Outlier
Decision theory
ProModel <Programm>
Relational database
Mathematical analysis
Hierarchy
Computer animation
Customer relationship management
System programming
Convex hull
Force
Newton's law of universal gravitation
Metadata
Formal Metadata
Title  Clustering (20.01.2011) 
Title of Series  Data Warehousing and Data Mining Techniques (WS 2010/2011) 
Part Number  11 
Number of Parts  13 
Author 
Balke, WolfTilo

Contributors 
Homoceanu, Silviu

License 
CC Attribution  NonCommercial 3.0 Germany: You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and noncommercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor. 
DOI  10.5446/335 
Publisher  Technische Universität Braunschweig, Institut für Informationssysteme 
Release Date  2010 
Language  English 
Producer 
Technische Universität Braunschweig

Production Year  2011 
Production Place  Braunschweig 
Content Metadata
Subject Area  Information technology 
Abstract  In this course, we examine the aspects regarding building maintaining and operating data warehouses as well as give an insight to the main knowledge discovery techniques. The course deals with basic issues like storage of the data, execution of the analytical queries and data mining procedures. Course will be tought completly in English. The general structure of the course is: Typical dw use case scenarios Basic architecture of dw Data modelling on a conceptual, logical and physical level Multidimensional E/R modelling Cubes, dimensions, measures Query processing, OLAP queries (OLAP vs OLTP), rollup, drill down, slice, dice, pivot MOLAP, ROLAP, HOLAP SQL99 OLAP operators, MDX Snowflake, star and starflake schemas for relational storage Multimedia physical storage (linearization) DW Indexing as search optimization mean: RTrees, UBTrees, Bitmap indexes Other optimization procedures: data partitioning, star join optimization, materialized views ETL Association rule mining, sequence patterns, time series Classification: Decision trees, naive Bayes classifications, SVM Cluster analysis: Kmeans, hierarchical clustering, aglomerative clustering, outlier analysis 