Document clustering (25.5.2011)

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Video in TIB AV-Portal: Document clustering (25.5.2011)

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Document clustering (25.5.2011)
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10.5446/360 (DOI)
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Technische Universität Braunschweig
Institut für Informationssysteme
Balke, Wolf-Tilo
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This lecture provides an introduction to the fields of information retrieval and web search. We will discuss how relevant information can be found in very large and mostly unstructured data collections; this is particularly interesting in cases where users cannot provide a clear formulation of their current information need. Web search engines like Google are a typical application of the techniques covered by this course.
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Web page Area World Wide Web Consortium Decision theory Characteristic polynomial Measurement Information retrieval Uniformer Raum Computer animation Personal digital assistant Internetworking Set (mathematics) Energy level Energy level Perfect group Physical system Resultant Physical system
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Uniformer Raum Computer animation Lie group Relevance feedback World Wide Web Consortium
So is all wasted by chooses to welcome you to a new instalment of all over the Web search engines and Information retrieve lectures and we ended the but sadly and the last time out would just discussing the quality measures off retrieval systems and were basically focusing on on loan to measures which is decision recalled and and will be briefly re introduced the matches so that we can stop off at the beginning and the end value will actually value matches so precision is The typical measures to get a feeling of pop free sightings of Paul Cole wrecked the results were treated by the systems are so high and many of the results that the system returns are actually correct And how many mistakes but we have but of course this is only 1 side of the mantle because you could you could easily get to what high-precision by returning only those items about which your very sure of what you feel is a queue the band you need something you might miss lodgepole should offer of some of the results of correct of because you would you would rather focus on the position of their a where you are not sure about something I'd by just don't under don't retrieve and this is why there is a 2nd measure that has been built which could recall ways they are basically of the reserve and items that are in the end time that comedy of those did actually Richard so what it's kind of like the number of objects that have been with from the user that the user might never know about and of course is a difficult to compute because you have to go through the and entire that set to find out how many correct things Aichi part of that and if you know that the man you can get a computer recalled and and for example if you have something like like patent Search Unite missing a patent as a very bad thing so you need a good regal
How ever what you would like more than anything else is kind of like the perfect we with the perfect precision so I want to rather than items and the reserve and items that Nothing that is incorrect in the scent of matching the great and the results but I don't want to miss anything that is correct that is missing from the list basic to what you do and the trade onto the trade so what you basically do it is to you have the full recall ways they were Edward levels of re call to reach what precision and then I get a curfew for example of the way say well at a recall of the 10 per cent I'd do have precision of ones so the 1st 10 per cent of my results are perfectly now like retrieved 10 per cent of the revenue and items and it dawned and and I think that is wrong but to go to 20 per cent of the revenue and items on may have precision of point data on 80 per cent of what I'm retrieving is correct 20 per cent off full to dropside incorrect item of but
The internet book eye retrieved 20 per cent of the oil correct items I'm missing 80 per cent of the correct items of and this is your feelings on how the system works and then I get a curfew like this over and that is kind of characteristics For the system and make it easy the say is the via have a system that doesn't like not The system in red is definitely better than the system and Blue because I'm There are levels of requital it maintained the higher precision So that makes it better obviously how and when to use the this will not be the case but usually but we won't have the perfect systems but there will be a trade off for example here in Iowa State where for hire recall will use and maintained a high episodes of precision rose for followed Levels and maintained their opposition from the world was more important for me if I'm a patent lurking are likely to find everything that in the system and definitely the higher recall area need high-precision don't have to look through to many items on the other hand it by doing Web search you'll readily look behind the 1st page of cool results that maybe the 2nd page The back it you don't want 1st 50 page pages of though there may be something that is relevant of your very much interested in in high-precision OK so than the recall values but the high-precision about you would be very interested as a trade of what kind of system you designed so the question what is best is over is difficult to maintain Suleman as the different herbs into said that some of the so this basic I we do you and them that several attempts to but
Because this is kind of a man with a graphic a reputation but also something that could be average could be somehow computed into 1 school number and this goal number band is 1 of Representative anacondas my Systems by this goal number and the number of the most renowned for his so called after the mesh as measured is just say I'm and high the needs of average between precision and re called the so you put the precision and the recall values into 1 Samuer and and you have some correct eristic parameters to switch between them to build more precise or more high or it called value so if I'd use in and out of all point 5 minutes balanced in all like here you you and you basically Some take a balanced means and just say if you stop in high recall values in all like it exactly if your bed a in high-precision has to be about the same amount and of course if you more interested in recaulk you can shift the toward 0 0 and a few more interested in the the position you can shift the Alpha to walk along and by shifting did the that this part of all this is pronounced no and then it gets and The measure of what we just use the average the means of the home on mean from well the problem is that if you to use the automatic means Them You basically would have a baseline of all point 5 for the mesh because if you just return all the elements in the set They recalled 1 that It doesn't really matter how high you precisionist he would definitely have an average of more than 0 point 5 And that's a bad thing of cost you which rather assuming that they face a system that really Saxton either 1 should be punished and that is basically what morning means that out
So I'm It was a very important is not only if you retrieve something all don't retrieve something but also in more positions to you some sort you could for example have a on a system that has a recoba you at 20 per cent from way below 20 per cent of the revenue items are returned and have a certain position of state 80 per cent meaning that 1 5th every 5th document is wrong is in which system would you prefer the system that kind of like 1st down and the incorrect items on you On the beginning of the lists and then stops with all the correct and 1 that kind of makes the items and and operate fashion offer some some the system that 1st gives you will correct items and and 20 per cent of incorrect Well obviously the Greg answer is that would assuming them to be makes somehow and the Good items for her in the early stages of the estate returned 1st The better the system it this a special each rueful Web search engines are were really hobby anybody looks behind the 1st page and the and yesterday that has been basically done for top care a trio so I have some K which is basically the results this so 1st returns document 2nd return document for the return document and so on and then you computer precision and wreak although you fall a set of the top came items sold basically if you stop with a reserve and item But want that the 1st ranked your precision over 100 per cent If you start with the relevant item at the 1st was in the position of the of the said And the Met documents you see Last binary it will be because of the chaos that you at least returns something that is really what it rather than before him around the cell basically the precision Kate and saying concept of re quality is computed by the resited precision and although you have the documents you Richard and still he reached the rank of K so the precision at full and the recall at full I'd take and those full documents into account and computer religious number of which have been right but and So that is basically the idea behind it It was in Britain's very for for example for the good of if you than what the precision at a in the recall of educated and you get position regal Kurt and it basically stops from well if you hit the 1st time values and the media to you start at decision from a 100 per cent and use slightly below Dalton so every wrong a result that the return of 1 of lower your decision and that's basically what happened so you get 2 big occurred and you also get this typical of set to shape because the precision
May drop without any anything to the recall assumed it you start giving out a couple of incorrect items for for example its assuming that the 1st item has been correct decision of 100 per cent that the assuming that the next 5 items of to and and into the recall of obviously because you did not retrieved in NEMO awkward items So you stay basically On the same line in the recall the but you precision drops A Kent and then at some point you start handing out of direct item again so you can fly relieving increasing your decision again And at the same time you increasing the recall because you did bombs and out more correct but 3 moving into that direction for the recall of improved free recoat and you moving into that direct for the group position which basically makes this difficult salt to shake up Kent and scenes of cost you don't want about means it looks kind of strange you usually smoothing the perfect time to make the move to make the most of Soham you can use the interpolated precision and certain recoba instead which is basically a high precision home for any recall level but the so what you do is you don't dumped on their and go up again but you would just and do it again which is interpolated for office of looks to the smooth and the UK Well if you can't play a few comparing your itself that all you some objects you you algorithm to other algorithms than decision recalled was the way to go the higher the precision at high Errico billions of various system and for example that we were talking about the track Conference of text retrieval conferences in last week and found and this is 1 of the up and up and up then use where different Algorithms for different work at the time of entity surgical persons of chemical documents all week Innamincka Makino like it aloft tracks and track the specific problems that you might get a point on the few do information retrieval and uses and 11 point interpolated average precision the basic the you take the different 3 called levels and computer and compute the precision at 11 points 11 samples of this week although he treated basic level Point here and now you don't do any thought to open and close the step function but you just Interpolate the through these points OK and some of cost you ever rich the precision well used over many different seriously that the average Pacific obvious is a again found and the and whenever you have a system that maintained a higher precision at the same Recoletos better systems and if you have something that is kind of in the safety of perfect some point out that you have to decide what you want by interested and precision and Blue Systems the idea interested in high regal the systems and the intersection point and healthy where the break up in the breaking of the of the matter which of the 2 algorithms which of the 2 systems you Good So that is basically what used for of finding out how good your system actually is in the system UK
And the last man are wanted today is kind of like that you have the so called me every decision which is a single value For assessing the basic quality of of his system and the idea behind me Everett precision is that you computer precision at case For a Nikkei's such that there is a relevant document this position in the results of the and then new compute the automatic means of for the specific value And if you do that for many different period Then you get to the media over processes So what is the usual precision for those values Wales have rather than documents return those ranks rather than returns and and it it's become quite popular to use mean average decision because it's but it helps you to discriminate between between different systems and it has been shown that has a good stability so I'm Basically if you try different during Then you get a different herbs for use system From That you have and how Ohio load you prefer is shows how good or bad you system So you could consider the area and the 1st as a measure a full hollow high Ohio loyal a perfect and this basic what would mean Evershed position expresses And the higher up you courtesy of a new system where a high this area used and has a high mean Everett precision and the red system The mean a rich processes position as the lower and this kind of book the the mean over the stigma just as OK yet questioned What the this this is a really good precision re colker basically of the recall him And of the proceedings Precision there Because you do precision at case The peso ranked coca but but he Yes OK so the next lecture of text in this lecture with a class during will move to a slightly different from area and talk about how documents can be classed and this is what we I do it now and I know them all the and luck
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Maybe 1 as the other 2 See this To get paid Here again But that part of it The if it At about that That night It A pale white do we do this whatsoever to benefit of do this 9 now it's going method itself is not a but touching the quality of integrated introduced systems that it has to do with the very waiting recaulk When you do that So the way is was a problem of evaluating the we called for few systems So far right so so if you don't if you would want to admit that 3 you have to go through manually through the whole collection with with which could be you which opened with a one man that you are not able to focus on a small subset of fellow collections and you just assuming that those documents return by any of the systems that are in these are to be considered 1 of the huge assuming that all of the documents those that do not have been return any of the systems are not red of this so that Britain some But if you use many different systems and they have been designed and develop independently and the rest on the rest on the Independent on techniques you can be quite sure that you do have a large coverage of already document Because I won't be able to be sure but that the best you can do so otherwise if you through the whole collection definitely which if it is not enough to point out that the more time to look at the last 1 we just and had a explanation of that was more potent 1 commission to Systems decision will re cause The and but it's and was a trade off so for somebody doing that and so Jullien research that high Ricoeur usually used the what you want to have that sometimes you even new even use bullion searched and had to be to about this the beginning of of the of the cost on high-precision is typically around required in costs such as Webster which recently want to get the 1st the 1st stage and find some below their results growing hundreds of results so as long as they depend on depends on the type particular you doing research on so it is very specialized on on high recalled might be that what you need if you will to to spend some time with results because it is important to know the answer to your information need but on the other hand if you just want to know some information about the popular take and high-precision is the most potent thing Whiteside where much
Then we continue with rest The throughout the world today want to talk a little bit about how to last documents in such a way that similar documents are considered to be of the same kind to be kind of assigned to individual Cluster and on the other hand that you can say about a move to different Clusters by get entirely different documents and of course This is the only the and that only only then interesting for retrieval system if you conjecture that well The documents the relevant worst respect to some information Or similar to each other They will look similar they will feel similar would probably used similar words and if that is true and timing of the said conjecture the hypothesis than last during on the document might actually help you to guide to some resident Clusters and by exploring the last he or she gets a good overview of what though of the answer to the information need actually so reusable benefit very much precision and re called because you she would get the complete lack last and and the book and in the last of the similar they all of our and and the interesting thing is that it's easy to finalise the so-called Cluster hypothesis so closely associated documents tend to be rather than to the same requests similar documents A kind of and and simpler to evaluate say OK contained the same terms the terms of the members or something and then are if it if that is true And is a good thing and it was from that is kind of like that you say OK as a real and lasting here and as a resident custody of the documents and the might be some some some real event last that these are totally Not similar to each other that is what kind of country but the high point to the if you and you keep can prove that you have to tested on Monday for collections and then you can say about it works out well does not work out for some reason or the other so it really is a higher offer and the extra mentally based not the cluster of this was was kind of problematic I'm because for different collections you entirely different
Document time so sometimes when you looking for a very very summer piece of information for example the boiling point of water or something this information might occur in the loft of a very different talking a might be some sort some documents I'm very very very small documents I'm 1 on on a very low level explained basic of chemistry and that some point refer to the boiling point of water but there may be highly specialized technical description of the at at this time room the sentence and it's insist mentioned that the body would for unlike like black that these documents don't seem to to have anything to do with the child cells that kind of like this very collection specific and and you have to figure out that held for you collection that this early and the representation of the documents a what the account of the battle for a model of how you may get a totally different a result land each you consider things like the length of the book humans and what the the document actually presses and of calls it also strongly depends on the quality of the American mesh what away due to account only the words that are preparing the really that interesting so for example the boiling point I'm and all these documents that contained the word boiling point of similar with respect to this work out of the can also say are all Cabriolet array of the document is similar on December the depending on what to measure a used by can get different world different degrees of great for my just ahead of us and it depends on the series of 4 sumptuous that might be true and for some other reason might be totally totally wrong but then Unite saying that it expected as an answer to a period of high enough but That is the thing that very of hopes that you you might have something in mind when he goes to sell up either by just 1 this information about the boiling point of water and the and the American what document it it across the you might have some sort of some idea of what description of something and has to be on some eyed and owner of this level of an expat level of the and and you expect eristics of the document already This usually indication that you cluster of this works that told for you collection and the calls for Europe information so that that say it goes off duty not from the family If you consider real-world collection the usually who have some sort of structure and put documents that charity similar cabarets in 1 Clusters you can strongly distinguish between some documents that used to be different terms all we order you something like T of idea saying no like the terms of more Discriminative less Discriminative also that can be put into his American measures to distinguish between should documents and so you have looking at we would patients led the way for example will find that many objects of many documents about transport saying the same information all expressing statement which will be built rather lookalike might be different and 4 meting them and but the layout of the basic the what that saying basically what that talking about A part from what you information was might be fares in question that we have a lecturer with this can we exploit this somehow Paul retriever and can we built Lovell was Based on this last point is the world of cost which struck you can offer limited Rea if I'd tell you last during the last of disease and the European whatsoever to exploited and so obviously there is a way and this is what we getting into a this lecturer and will be looking at some Applications off last during a we will than state the problem of what Aclasta reusable what described the Cluster and will look into 2 different public after the flat last 3 and a Hierarchical Clustering under review that some of the algorithm some of the basic over part of the Boston so I'm that has been done information retriever especially in and Web search engines
It is that you found that that you try to get documents from the same Clusters The rope that you kind of get document from different class does that are highly Parliament to get different view of certain diversity of And actually I think in the end it back in the 19th it stopped when when people a very interested in this in this plastic algorithms and some of the Web search engines will claiming that Clustering provide better results the non last autumn results in terms of similar and in terms of diversity of all of the results from the UK and of the examples for example the last 8 of the of the search and the where you type in and of a certain what a which life in as it is with great citing in the statement said turn the about like that you might be interested in for a company interest Mead by and then it was you mean late now did not and don't want to buy them on the day and don't want to shoppe for him because he can be brought on But here are the results of the found you might find that well on do have a degree of P E entry which is 1 of the computer science community of the graphic where many of the publications are shown and for example 1 publication of game together or something like the and and you might get my home page at the University of Hanover where the of awful for Research Centre and the might get out of my head Homepage he at the Technical University of untried bomb where by the Institute for information systems different results the pages To look at different from each other was basically reciting bibliographies that the 1st 1 1 tells you Balthazard where and how wonderful the research centre is and the 2nd round of might be Steve because it tells you how wonderful brunch ideas and how wonderful the incident so that they might be rather than from the same class of the of my from different but they have something to do with each other and and visualize in the SCF classes and telling you might 8 the retrieve was happening and it is that the last does not only give you the results But there so so called faceted Fastest has said the fact that are kind of the meetings behind last so they get a different Clusters for example of the of the research centre here the technician the from tried here some of the papers that have been accepted here it's a of mine Unite of these different topics if you wanted like that that had in documents that seemed to be relevant the suspect a mining and not only sensible so for example if you have the best people over time which doesn't mean anything at the public named thrown together the documents for last and that is the 23 document that simulate was respectively Costa I've no idea what they are now that they seem to be similar on a text level And they don't seem to belong to a sensible Cluster because it doesn't have a good heading off the engine did not find that and that is something that does seem to be very sensible in the UK and if you looking for a certain kind of information so that want to know about the publication of a book a want to know about him as being part of the University of Bradford then this is the way to go and you can late on any of the classes for example this year and then expand the list of the 5 documents here that all similar structure and and at and that will deal with both to buy being density of University from a So as the application that might be definitely said sensible and a what what you get from it is that you be able to spend a few coherent groups so the of the document or similar you know you get a feeling for what you can expect when they move to the left the and you don't have to help from document from to document if you I'm in and an impression of what you can expect and looking at the different class said it gives you a I'm notion of the diversity what you can expect the consider what I've played some of some kind of a hobby like of items scuba-diving order and would have some by an pages about my scuba-diving photos fish photography of underwater block where
Then knowing that this is a fact of life would give you some top level information about the into 2 you go into law Like beauty it just get the public with information about something to do with a research centre in hand over and that something to do with the University in front of a sell it is symptom some some inside I'm on the other side of what we just is that the labelling sometimes you now difficult and the best without doesn't mean anything and and high tools the correct labels to take the word of cream or if not all of you take some works from the heading off the side of from the document title what I do you know that it's not really it's not predict the bomb and the same goes for the quality of the play after hobby find what is a good quality which document simulating enough and which documents on December that enough to put them into different last so these questions that we will have to to answer some of the summer if we look at the tree Apple
We might have the Apple in my weight by might have Apple Computer Systems they operate is the apple stoic by on new Apple Computer or the new life from the iTunes was music in mind when might have long of things and the context of what we have my might not be Easy the recognisable publicist Usually this is done by the size of the cost fastest so I would start with bigger clusters of business and the more so by the number of people that seemed to navigate into 1 plus or the other but is that a thousand people on the Web asking for iTunes asking for a new music that does not mean that the idea of got not and not looking for my next Appletree that and going to need for my from a problem and this is kind of an tricky what's what's happened there
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And by time focusing on the document he would see that the system with your preference and stopping from that document You get a class during the kind of few gets you give you deep inside the in left last this document might much much relate to are much so you so you search by navigation by navigating was systems and about I basically do is you take the whole collection of human collection so the way for example and you prostrate on very broad-scale so you say this is documents about of computer science is a document about adenoma methods and Physics he whatever maybe and then when you click on 1 document yellow Computer Science document than the Computer Science appearance up read last updated and last in smaller entities might be computer graphics and my information systems that Microsoft engineering as up last of your your global so many basically do it if you stop by last during the document collection smell number of plastic and the and the user may family the by selecting 1 or more of these The selected Clusters the nomarch and that again and this is kind of like the sketches and get a step so and scattering the last of the documents that the user has selected by basically selecting 1 of the class of 0 1 or more of the cost you scattered into different sapped classes And in the gathering step whatever the uses seemed to find rather than this kind of combined to 1 big Cluster again and then it goes on until you find the satisfied all you don't to documents way talk about individual document users have happy so for example if you have a New York Times stories and you might plastered on a broad scale and that where these all the document prefer but there are some that are education some that Germany or and the user may now to some of the fastest might be interesting to see if the information needed for example of a well I'm in 4 mime I'm interested in how the year awkward for all the rock crises especially with with was the suspect to the Orioles I'm reflects on Germany and state that she was this 1 This 1 and this 1 which is to be result was 1st or cumin of all the cost humans critic education document and now This began during step the and the documents from these 3 subclasses emerged together and may be that could be called International stories but how the Beuno and you don't have the label for many at this up group of documents and again music last during algorithm find out how they could be on how they could be tested and sensible terms and this is actually the sketch face and you might end up with of as a model deployment in which the Germany is alone in Africa are might be in order to might be it hostages may have something to do with it but now and then to select the next Clusters for example the you shift from pocket stamen Africa because you by but by some high interest in game now and then it goes down again gather And yet her again As you see the scope of the brigade smaller Your very brought up here you much smaller down here but depending on how you documents look like some of the things may come in that you did not really know does all that you did not really of field feel that you not really interest of also for example of naughty away Trinidad the CIA small 1 and review the must have been a lot of documents meeting with some incident and and their car of related to what he for of Kent And he just navigate through and this is basically was called scattered with just the But sometimes it hits it's really this very very sensible and 2 because of the document collection hierarchically so for example if you Coogan you you have to ride the waves say OK undaunted top stories wanted refined to only top stories in The you S although some are concerned that the 1 the the business all of what the science and technology stuff for along the sport in the last 3 into into different things and this is basically a frustrating that politicians the document collection
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The last during the collection in the last enough of a difference collections as special useful I'm if the collections contains small numbers of top end each of the topix is covered in and in a different way now in a similar way within the last but differently from the other last so you should be able to distinguish between different Clusters quite easy and you should find the object within a single Cluster simulator And this is this kind of conduct and the idea of the cost of and it's still still in use on a broad scale of the losses so called direct to array object the most where people were trying to build a I'm by a hierarchy them basically a table of contents of led to a classifying websites and think this is a website that deals with the health problems and basically was medicine of something and they were assigning websites to these labels As to allow the navigating approach to what kind of what you need as opposed to the classic books and yet who was doing that quite quiet of for a long time they go directly Structured the muscles doing so I'm but that's kind of whom 1 of the vision that basically never enough so of this not so much about Clustering as an algorithm a finding similarities between documents that this is rather about people at the urbanising things and higher rate people assigning last ahead to documents and take these documents although some of the documents all about business and and the big advantage is that you can basically navigate for Load document collection that you can't that I can use browsing and confirmed Wales browsing as an approach as an excess half to to inflammation and you don't have to bother with the reason refining in terms of finding of things of rather than because he didn't mean it that way the for example a few if you now search for Apple and you mean Fiona Apple than you would get going to the time that you into the entertainment column and look for the entertainment thing and probe not get the computer at the the tree and if you want the tree and you go into the on and on like calming section of the home section home not something I would find it that some but as they offered can see you sometimes to 2 pm to know all the direct to restructure From where you would expect something and if somebody has a different concept of where to put it still somebody might for example be considered gardening as onto the odd of gardening now and then you find them under which for these not what you would expect he would rather find it in a home or recreational what they see that probably different possibilities getting to way you want that I'm the because during collections can also be used to extend to search results so for example if you find a number of documents that match the tree is a world does from this single relevant document
Maybe the you want to know a little bit more about the topic I'd eyed and find anything more Then across during and finding documents similar to this result document Might actually gives the user a broader you on the public that the results of the other results are on offer in the specific with a was respectively theory rather than The simulator Ritchie for the results of human might add a I'm well of feeling of relevant and the and the idea so if he matching documents and this is your a furiouser that you can extend your period of all the documents and the and the cluster and try to give the user abroad Overview space in the idea UK its also interesting to note that he had gained some speed up in retrieval because and finding relevant documents if you found some something about will probably not be that you have to to look through a full collection
But that you basically have to navigate in different area of the information that the 1st of good if you have your like where the with the terms of Term mattresses here I'm and these different terms to 1 to 2 You might have a different documents here and maybe at last year and maybe last year and in the documents was that the US does or this similar between last So if you can go down to 1 Cluster you can truly large parts of the space where do you don't have to look for for 11 documents and this is the kind of the idea that you say OK by taking the points for the club during the may be existing documents may be documents that just came like the central itself of the classes all whatever of enough and then I'd find the Clusters having best matching representatives of the city while the pre match is perfectly to this cluster bombs and then icann room While these document either have to look at the medals and just look at these documents and this will be my results of Kent This can be done and it is quite effective because it allows you to skip Comparison of the acute to on May document and the and the collection and the retriever exults if your cluster of this is that it is not different from what you would get if you to the global competitors Of it a cluster of hypothesis not rather than the good of the not to help them might well be that he is the 1 document perfectly matching the But if you consider your like if something matches the theory that it should be similar to the documents that match the to and this is a good way Quote
Look And with that we go into some examples to see what industry has done with Clustering search engines in light of the early and love and the is a plastic can be used so hot practises but different of some players you that kind of family code costing at the example of the previous light so close plus deer and broke for some reason and all their pupils some Australian company now called the baby
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To have some some specialist at the items so they find person that are related to on to a billion somewhere I thought would expect some some more year for some some politicians Of the other people in some of the stuff president of Germany in the United communication is strongly related to Berlin buildings in the in and globalisation that are in some way related to the use of some of of the stock but to buy on a day after Nicolas in the on line not think they have the have often not office in but not them The cause of the city of works but also get a deeper into these customers than Tutsis some law more cases related to Britain And navigate whose son who wanted no mobile cuts back Then You should be paid to do something Yes Tank The words of the then however whether 4 other related where that kind of pressure on them to comes buildings organisations summary so this kind of complying with the day to to see some of connected and relationships might be a good idea if you should to 3 some brains Domingo want to see relationships between comes from the doing research on and that is why I'd lower has to of
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So I'm not worried after we the application to get down to some great make devices and see what actually problems seems to be so I'm the issues that you will some have to decide on and plus is a kind of it it's not a traumatic everything and you just have the stuff during a group and we do I'm everything on account of her to be a serious like many classes do you wonder what should be the fastest side of is the fact that as the hierarchical Clustering so he won the navigation half from the last 2 smaller clubs with the on on 1 level the I'm but just depending on the on what I should do it should be a half Clustering should be soft lustring meaning that I'm cha-cha-cha every document be assigned to 1 club and 1 of the only old could be made fastest time a high a hobby bought the quality of the last trained and of calls in Britain a terms hobby you find the actual Clusters you arrived so I'm for for question about how many classes do you have a will in the following just use Katie as the idea of how many Hugh 81 and that the city to different approaches you could either state and 5 last want 100 last that means you define the case before the searching for Class during and you only consider those Clustering that result in K classes
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Stop clusters of what expect of care and the classes gets more and more of until your own document level and not left with other No 10 documents that you might inspect and would be high rock from the good thing about the hierarchical cross drink is that you can never get the suspect document so you have an entry point to the benefits of all the documents and the collection and then you decide for are or want to go he here want to go here and then I'm document on because the documents for want of this is 1 and a prolonged immediately all those documents that
The United way of navigating the document question hot and soft lustring means basically I'm for the half Clustering it you assigned document to exactly 1 Clusters and it Conway of all 4 of actually doing so you decide for each document about where it wants to buy if you take a soft Clustering than basically each document gets the distribution old class so it might fit into some Clusters it might not fit into other Clusters enough but for established a think about newspaper or tickled a year yes it might be an economy optical but it might also help
1 of whom mentioned some countries but might also fit and and and regional to graphical Cluster that it might be concerned about some people also that would be a class classification that he could use the a class ring that you could use and then of calls every document might it several purposes might it would be it would be good to have a different ways leading to the same document time and not be restricted to know this document is about the way that will would not and fight it under you ask that would find it on the economy because the talks more about economy than about the the rest of the year to decide if a make up some rules but something law might not be worse for the staff Clustering it is their fault that his you to for creating browsable so you don't have to decide everything in advance and once you made 1 wrong decision new looked for a while the gardening all for you Appletree left under under but under some Eidinow hobbies of cross so something like that and they will never find your apple trees and because they are not accessible with respect to the other party and a 1 example for of the of forest lost the late semantic indexing where the documents could be about several topics so we had and the Distribution of high or much of the talk was in which the human and 1 for this is kind of what you do all of us as the problem statement what you have is a collection of and documents And you specified the type of frustrating but you want of solves the before before and on and then If sunk But This kind of used for assessing the quality of the similarities Between different of job Hughes recalled objective should go for what you have to do is get to find a across during that minimize objective for its depends on its bid on simonetta dissimilarity enough volume might want to maximise the what most similar Object being together in the 1st you 1 police dissimilar of Jeff the to the you maximise minimize the of different perspective And we don't want to review was empty classes with a week to save the life of every document that has to be the closer and every to At least singled out and the problem of empty classes is them it is usually quite hot and and that of some of the respect of the season we will go into the complicating matters more than actually need and so the question stays that what actually makes a good last how can we evaluate the quality of justice and a think we we already agreed that the 1st Test to some of something to do with the symbol Aridi function with of disability function so that the Massimo object of across off the Bedarida and of course the more distinguishable different class does on the bed is a pen and this is the idea that we want to have is low into the system enriching so between last familiarities of documents to be very high so that should be part of and we want a high in truck last December American so ability within the cost should be
As low as possible 0 Compact Cluster and that of spread L the place of care and are very much distinguishable what If you opt restaurant structure Look distant should be high and well skirmish guests are of a mesh off distant so what too weak to walk SAS Preston Liberty and the books to be key And the 2nd highest in the UK a few talking about distances justly of way around within the cost low distance between Clusters hiatus a
So I'm not if you look at some some into a into summer than you might find for example that this 1 discussed during a pudding all the documents and 1 single Clusters is a that Clustering because and the distance between these 2 documents It is very high and the policy same cluster this threat If you see this combined become better because it now The maximum familiarity between documents and the cost is this kind of much smaller than it was before Off less if you Computer on human now you could say what through the worst of the strike last summer of the UK and that is really my clusters of like that are not amazingly similar to each other because of the same document but of course this is all a sensible thing to do because we can see the truck of celebrity is high where's the into the history low of We not into the system that is very very high still is almost as good as the in truck of cost 15 impressions of that the security of which is to have these 2 and the Inter quite low to of this is basically the idea behind after could was was a trade off the or a trade off between maximising the intact last December's the intracluster their teeth and minimising the Intel justice and depending on whether you say well and the case is the fight in advance and you have to to live with Kate USTA's whatever the that points in not only the state while depending on the day point and and determined somehow the I'm the size of the fastest and number of the last leads to different algorithms that it can use to common secondary goes or you know you should emerged the very small Clusters so Cluster that just contains a single document is either a complete life so to document that nobody for about except for 1 person creating the document and that is a very strange and you should avoid very large just as it is them
If you documents that are very similar to each other pudding them into a trust is a good idea but it too many document up in some first than returning the results said the user might become a big problem so maybe they are similar but may be not to similar you might find a way of breaking down the to allow for that efficient retrieval results for fish returning of the results Up The use but follows girls are kind of like the internal structural criteria and you have to and figure it out The extra Criterium criteria and the kind of deal with the problem of the quality is usually comparing to and from the what makes sense and what does not make sense for should be to get a 1 of the and what this kind of like the idea behind the most but the yellow direct recent stuff like where people actually assign websites to serve to certain last and if you can read this class during by some some some automatic means of and this is a very good result of this because of the fact that the human and but so so I'm having to find a good gusting what has caused this or with the that use approach to just Clusters things and other Treasury although this a certain number of ring that you can have a some Calculate of the of the update the function and you take a discussed during the minimises all next month straightforward right to appoint impossible because high many ways of across ring document collections you have depends on the time basically hominy Costas you get so you take some of some of the and how many documents you have that you can put together all put in 2 different last and bomb
The number of plastic that can be derived for making cake last is out of and documents is a fan of the game by the by the sterling numbers of 2nd kind and roughly the starting numbers for me to want to stay area and the disturbing numbers out roughly exponential in the number of documents that need to be so the Moulin documents you happen your collection the many ways they are exponentially so to shift the document in a and all the Fast and this is growing very very fast so we can really cut out the mighty approach to the way we can do that we had to rely on some heuristics and we will not look at to conduct after England uses look at a map of possibility to derive lacked class drink and then we will look at the possibility to derive hierarchical last so the mesh common and most read known Algorithm for flat ring a so called came means of strains of paintings of everybody here ever heard of the game means
On 1 of them at least useful to let them yet it said that some of the most appalled hot frustrating algorithm that means that we have a set of document document collection and then the last during capt salt certain set of documents and we defined the number of Clusters in advance and we usually represent document as unit factors but it doesn't really matter I'm worried do is which tried to minimize the average distance from the fastest so if we have documents in space in the UK this year now that what we want to do it is we want to be rise Clusters your such that the centre of the US this Show the minimum distance to all of the entries Over the last 4 of this is kind of the idea behind this Scaling means that still looking at the update function weekend allways relies on the US centroid of across the which is basically bomb The distance In took full time engines so we and diamonds can be the total size of look a bit various so we assume the but the space model but we have won access for every tournament and we count only excess how long this term Kershaw about an hour and about what we do is we take for all the dimensions of some of the distance was the spectre of the statement and just mobilised by the number of the men's and this is what the centroid of a cluster of wooden that for all the document of The same goes for the flow of the city of some of quest to if we have a document Cluster we might was the spectacle of the documents take the distance of the distance of all of the points to suspect to the last and then computer the squalor Naum about summit and this is kind of like the mistake the total mistake we to for this class of car Carol So for the documents where the centroid is basically the average of the distance with the pulled different demand And the myth take As part of the quality of the trust is Some of Where's of the distances of each point in the 1st with respect to the centre of the world the point exactly in the central at of the cost There we of perfect during a deal some of this 0 but assume that based ghetto around There The scattered around the central would dispersity of some where grows and since its of some of Where's out life has become more than objects of very close to the UK it is a basic and the behind
So I'm thinking means that throwing we consider the quality of the frustrating for the key different class that we want to do right as being some of the City of some of where for all of us So we want the class strained that has The these Chris Eagles some of Where's for full of of OK so you don't want 1 particularly good class and all the other Clusters sacked but on average the ship because every just should be as good as possible and are minimising the value is basically Yet all your estate some so we trying to minimize the average script distance between each document and the central Which basically means that we want to assign every document as close as possible to the centre of the trust that we know that there will be cake a Clusters but we don't know this centroids yet so what we do with do while we use a multistep algorithm that stops was a random class during and then refined the last a more and more peace but he until it some all stabilises in this condition where every document is signed with Luis quadratic error to assert that the set up at the bottom we do we take so called seats which is a documents K points in the space of my which
That film was centroids And then we create Kate last those empty for the time being and into each Cluster with put 1 office and so now we have a cake off those with 1 document the each of causes classes have to plus or the documents and the collections for what we do for every document in the collection we determined what as the next Clusters if chill the cake left the centre so it's cake distance computations until we find which after this document should be assigned to a pick once led the documents Are split over the custard then We can recompute across the central Because if we took some bloke you mins The choristers by suspect was central so document of plastic may not be centred around because of the cost us and put you in the book that made show some some some some buyers into some sort of record holder may be picked up like than the classes and for that was initially chosen randomly May not be that animal we recompute the and cheque without Clusters satisfying ority it's not satisfy we stop Croesus all over again with the new which computed central update are in no way are we doing While we assigning all the documents and the collection to the nearest centre For the nearest central has exactly Exactly we want to minimise the Rosario some of Where's and this kind of his secure its stake for the signing them to exactly Cluster was the nearest centroid by the question of is what is good enough but we can't say what I'm that the change off the centroids when recompute was not so big so that the string will be all the same That could be 1 way of saying not where we have a maximum of iterations over do that 5 times and done or that we are thing about the the Rossio some Where's and that is small enough but we just stop it and what I could do it for example use we'd just randomly so I'd just go here and take some of the classic centroids like you and the other 1 if you can't do we now have the most to leave and go out there and doing now is kind of like a sign of the times the document was suspected closest the for example this is close and this is closed and this is those this is close and this is closer to see what I'm doing this is disclosed that this was the skills of their way to the next
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The only difference is that the Met Orakzai existing versus centroids and not existing huge so if you want to visualise York last by retrieving a document saying this is kind of like the interesting documented this is the document that his representative of the last and came out of it is a good thing If you don't if you could just using abstract ideas and you can use a set of goods and that as far as I see means that during which is kind of like exactly the same as a means of self last so you basically can be separate Clusters bomb and model based frustrating out ways you that some of the data has been has been generated by some random distribution around some unknown seeds that amount centroids and and basically about what you try is a maximum likelihood estimated to find those scale centroid that are most likely to generate the opposite the soul of this doesn't kind of punished old life so hot but rather looks at where the masses of the last and The most probable cause of centroids public last is considered to be in the middle of the night this is the ideal next up his everything Kim questions of the food like to push on the coach Well but they are buttocks with But
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Then the because like like its Reid has a rude at some point in all like and but it's kind of like the debouches into different branches in all like and sort of if you Go upside down but looks like a fruit trees and I stop with the individual documents and depending on the measure of simulating between the document different clusters of for example the documents that are While taking the title I'm here from the from the Reuters collection All for us not recognisably different from the kind of like different out Ticos carry the same type They show a Of all the allpoints 8 5 for something like that again and they are the most similar document and the collection and that they will be united in to a single up and and if we do that for Lopez of documents we might find out that the other documents that might be United at some point a and that we might find out that also these 2 Schirra certain simulator and at some point we will arrive at the root and this is 1 of his key allies works for Liz example has been built on the road as collect the unit the cold because I've come to the cheese and then we had like can see for example he said hold interest rates steady and the Fed had to keep interest rates steady at similar to update only a slightly different wording and if you take the hold here and keep year in all like them the cold and some of his right some opened 6 8 and this is exactly what 68 when the documents on which of the concept of a dendrogram is the where other last does in the end of crime Went hominy Clusters are there For Haiti In the end it's all just 1 big clubs the best Yes spoke So when you kind of like a split like here than the Sue documents for a new class Celestia but how can I decide to hominy classes do have for what is a good club The over And you build on the fastest but there is a question of numbers of steps that you did well what is that Special using about the quality of the 3 With exactly for this kind of the quality excesses that her behind a upbeat go on this for the excesses of the more you go into the direct Lilian number of Clusters become because your putting more and more the December Document to get into Cluster so using the stressful for example you will be 0 5 for something with a 1 0 lead to have a document Simoneti within classes of all point 5 whenever they mean all point 6 0 where it is and how we do it is we have on this level through the dendrogram and see the fastest which are basically intersecting this life and these clusters may consists like in this case only of 1 document the 0 there may consists But in this case of several documents depending on something merged below the all point lifeline or This is the idea behind the dendrogram and and and using the shuttle for Germany the quality of the cost because the medal you take that away the Mark the students askew have where something on a very high level in familiarity already merged the Bedarida the bed a closer to be because the Eden try plus a celebrity with the very high on the high you go up less in truck their the there will be and so it does not depend on the number of Clusters all any number of the suspect defined previously but it rather depends on the on the deficit but the depending on the data on the document they might Marche 30 a new dendrogram Oldhamite and if you look at this year for example the and most of the last 3 most of what is actually interesting in terms of the strength of for Class during all these different documents stony at the don't want to look through by hand is happening somewhere he enough so that is where you again sensible number of Clusters like 10 12 for and this is already a celebrity beyond all points you open to quite quite know already but I would hope for is that the fastest Murch But it depends on your of depends on the 1 you The But a collection of American between uptick so I'm for established take take different different sizes she now like and and then you define different different ways of of well different vessels so that for example disappointing gives a cluster of high 3 because the street document output to get them into a single last he And this year as a total line gives 17 Clusters because 5 6 8 9 10 11 12 13 14 15 16 70 because into sex dendrogram 70 points Full the documents down here full into 1 of the 17 Trust which has a Focus This is basically a case and have and we were talking about temerity of classes and the and the quality measures for from good Clustering or back lost and the question of causes is how to how to compute the seminar to between just under
We can't translate this questioning about how to a recall the disability between documents because when all similar documents off a can take that receive use some Quinn was suspect some some some centroid and and then say how or how good class 3 are below the rotisserie of some where the better as far string of the city and the different ways of doing that and and the basically it comes down to full of ways of doing it single interested in completely busting centroid strength or the group Everett plastic and before for example guileful singling out string than the Simoneti off to clusters of central is given by their most similar members
A So in and in this case we don't consider how far these are part the don't know all but we will know the 2 most similar things this is what finds the Cymbalo to use of the total last book and so it's a whole close the Clusters does come to each other if you singling Clustering it Kenneth and and the idea is that if I'd take new points if I'd to Clusters that very close in terms of singling out string and put in 1 random point could be kind of all the Treasury whether point classified where point that it into which point for because the of a close to the heart of 1 of the problems of course of singling Justin produces off long change because of a stop loss during their World basically out want a want to put their the documents together that are beyond the certain threshold and quality of the McCanns well if I'd have a number of documents here than These documents are very far part But the using singling class string himself while these actually quite close so they belonged to the same cluster these are quite close to lay belong to the same class and transitivity also the 1st point belongs to say that these are very close to the use of a close and finely The edges of the change the end of the change may be a part 35 but it doesn't count and singling plastic at only take the closest distance between 2 of between the UK and that is why singling plastic may produce some change but on the other hand is is kind of like that but I'm a good idea of assessing how much space they is between Fast how close to justice from each other but only doing it is completely ringways non or not and the simulator team between classes of the most this similar members want the furthest distance there is a maximum of the minimum Would be in this term the distance over here and the problem is that completing Clustering really sensitive to outlaw because if you have 1 point that is not really very close to the centre you get a UJH competing dissembler which also is not what you would you would expect and when you classic points was completely plastic the basically say well for my for my documents year that for the chain before and that was the completely was the singling Clustering out well take the minimum between between point of the cost of would basically the 1 classed as a well OK These belong to the same class because their quite close close and these We don't to simplify might be on to simplify because there are close but still have to take the completely of this would have to be quite close so icon put all these 3 things in the same class but have to decide on a morale this is 1 Clusters because this does not work American to stay here and took custody the approach and the point is that every document that is outlier so document so far pop scented old last because nothing for a pink might lead to a US splitting into a many different Fastest usually get life of containing only very few a pet basic idea of competing just good But we of the central Clustering reconsider where or basically a don't care about where classes come very close to each other over but there are help like the a man of the distribution is what counts so what I'm doing is take the centroids I'd take the centroids of the last this end basically consider the centroid The distance between the central it as a measure of into struck the of inter last Zimbler to the further the central for of the masses of the distribution or a part the band of the strength of the What I basically do is you take the seminar to between every 2 victory every point of the classes and he just ever did and this is basically what he sees in the last the similarity bomb The problem really is kind of like the found that that if you use dendrogram you might find that a similar other Clusters But the fused at some point Was improving the quality of the pasta and so it's not a mob Tomic decrease in quality any more of this kind of conduct and the strange thing because the classes emerged after the fact and the politics of the improved with just kind of had a very unusual time and you might well wonder from what I can also do it is kind of like the so called group average Clustering sold the similarity of 2 classes basically giving by the average of all the simulators sees it take the simulator teased between the cost and you take the celebrities within the classes and then you body average and that of point is if you have Clusters that are close together and Classes that are different classes are all then he will have a good Kustra how ever since 5th take The Weiss distances between all the points for of the 1st it's a very expensive computation him up road The load different ways of computing the sum of fast
Well that was a glamorous there was kind of merging Castle survey close together opposite work top down part of work which stopped 1 closed and and go down by dividing classed as I'm and and how would not give you the details but basically it sits at the end kind of their similar to to toward waste and glamour to the US for example I'm we could do at to means the strain on the classic containing all documents then we would have all 1st split up and and for every of this up last week to ban to another to means tested which a basic but into full of and we get dendrogram This is what could be do again you might of some trains the smoke such appeal pointed Loblaw blow up of structure constraints and other took questions to 0 devices for a plumber to every race clear about what they can do and what they should do this But what about the British Yes some of But the mass of the question is whether the mass of of fall from a during the quality its kind of some used for doing the frustrating all for just determining the quality off word but it in a way it's both because if you use a came means Clustering you know like you have to assess somehow how how good class and and and that is what you do with the distance to the next centroid duel with the distance of next mad get that you need a matter for the distant and of course you can really use the distance of of finding out how good the Clustering as the programme would have a came means testing it could be a full means testing could be a 5 means that could be a six means tested resulting in different Clusters because you have different numbers of petition still was better a than Fleming Clustering fit Europe that distribution better or 5 means of Sir Howard you now that you have to compute 1 number all the kind of like a set of numbers to to find out how good your substring actually is Using using the algorithm that he decided for a and this is what we did before so you could use singling clusters of completely Ingle grouping group Everett of this kind of the idea but with behind and depending on what measure use your punishing all re wedding different things that uses singling Clustering Europe kind of like a punishing just as that of the cells that shed some items that very close together if you to complete the task brings you you you all those last I'm that other very compact and so on But we are way out of the basic if you if you take the hierarchical Clustering everybody can do it at home I'm than you stop with the number of points and you kind of like built your last time for the variations on how the of Elliott the Clustering we use some internet criteria we use the central Distin's all whatever it may be that you could use fixture no criteria you compare it to a manually crofted frustrating they I'm kind of like a table of contents for example of the 1 1 1 prominent once local BrandIndex but you look at the payoff documents and a new look at 4 percentage of Paris in the correct relaischateaux is head of documents which is kind of a very similar this is in the same class and this is true positive this is what you would need to pay correctly contained in different class this is true negative if it is wrong to contain the the same closer to full for the trip and the of Paris wrongly contains different classes of for negative and was the sole things you can do precision recalled as we did before and is basically a case you Comparison of how good does your just algorithm reef lacked the manually assigned ground troops that you might be interested aka and so that would closing for 2 days next actually on residency back and we will see what the where all this lustring goes to unite like classification how do we deal with new documents and out what what the interesting part of the UK
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