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Document clustering (25.5.2011)

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Automatisierte Medienanalyse

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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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Discussed for a time and 2 because some questions on the case last week with seeing language most work from basic early we try to estimates somewhat for of the soul but at the scene when the just use the related term frequencies as they appear in each document we get some kind of bias estimates the what exactly as a problem and what can we do about it The thought that there are a lot of during the World Cup is the focus of a lot of work to do but you I have more of the ball 2 1 problem destiny on 0 estimates because they they've a full set of flights to be 0 and all of this lead if were does not appear in a particular document but there could be that the the document pulpit is still related to the stock and so it's a good year to use a non 0 estimates for those terms on what about what about the we weekend can estimated by the frequency and that do of to appeal in the document would woodworkers can can be can be seen here It will be object of the mood the the fact The so Orion there could be some kind of over or estimation of from the to estimates could be large so the general ideas to use smoothing on books some we could use the the general collection frequency of every time to a squad tempted to appeal undocumented down and the temperature the Earth command call them and it up and then we get quite good estimate Orion X 1 9 Long you their example of your expand the difference between relevant and coaching But the your ideas Homer But it up Yes kiss that all of you So what will be be a lot more of a pegged Gaibandha back some below that it would use the nett There are were a lot of looking for a while Yeah that's exactly right so making the main problem in this distinction is that time and that end only only on the 1st of those document as being a good results that contribute something to the knowledge He is an example of all that sold and the distinction so usually when 1 talks about rather that is the stop Akyel subject but events with cost last week and the and and that the public and copy developments in the 1st Test is sent to the state of knowledge of the of the user of using some of some will some computer scientists or someone who has some by some background from taking a background economy has signed is looking for followed their pay trained on the review of the works of a trend is famous algorithm and that they will be well below par about it in the few weeks and the grid and the grid and could be a trick of the residents of the ghetto scientist expect some technical content extending the man behind this idea was hot implemented but of 1 result without could be whose owned overview of this technology which is held in generally understand time so and the terms and losses to just just tries to explain the idea which presumably is already known to be designed to so this definitely would be relevant results because it fits the grid patron of the rhythms of a topical sense but passengers signed to soaring knows about what it is and just wants to know hold works that this is the biggest not time rather and in a sense of urgency Is the main thing to be but they are not because it was seen how to evaluate different between on Britain's only certain the data showed an example of how this usually done in the track collecting and found that we had an example of often information need about endangered species which has been described being using this containing 3 points that tied up information I Description and integrated Sonia task was to think of an information need a film describe accusing the team but to do that A Caywood Zizek some both but But part of what it you would will see Look how Part of the novel that Gained By the time of that The also the main man in the main part pointed the neuritic is to make clear what is relevant and it was not until you make this very clear so someone else could easily decide whether some given documented rather than for the information I don't want so that the most important part of the of this information need scripts and because the idea really need this to to evaluate rather events in the UK in a consistent way of redneck some of whom from some time ago so that has been some incidentally Iraq where some Tuiaki don't list which through at the end resident of and other side of this committee could be reactions to put shoot incident so is a description what happened so in December 2 thousand 8 and rocket on the 2 issues that you as president of the Bush was giving a press conference and the and the question is how it International this event of the year is the most potent parts of the media commentaries that are as well as documents summarising but with the media said about the incident so that contrast on goods that only point about the band itself but do not comment on its on offer and so this is a clear distinction and should work fall for most for most of the cause this is usually done you evaluate information in X 1 when the brewing method
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
Ideally Clustering should do walk bought the Coppélia the manually over in the December duration when they use is kind of like was an apple the Apple is the tree and the fruit from the tree but made a full compared to refer to different companies here to incorporated the computer company pulled various actually a from the musical science fiction film the apple from the Eighties can recommend everybody to watch it and to be very interested in they are music on an appeal the by the model of the and interesting that the records were also 1 of the earlier record labels and the Beatles on the field and that will not just some of you may know enough and different ideas of what Apple might prefer to and Wikipedia's not pudding and stress on a figure out saying you all work well had read your automatic users and you will need to computer company But this a well that is a mediated for you and then you choose what every and these out of things that we can sing got And if you have a obscured notion of 1 at what else Apple might be used for example but it might be the Big Apple in New York was something like that and then we don't paedophiles A Case Of course the truth slice somewhere in between you would love to have a research last by sensible last a recognisable clusters of different you which indicate Pheidias manual work and of course I would have liked to have it done automatically some Hall by collating the information from the lead from different people from what they are satisfied with what would result they expect the stuff like that this is the challenge that the testing and really have to work with this basic what need
I'm Whereas the 1st dreams but the numbers new wealth British now Figure that 1 of the 1st assignment off of last during the The useful techniques and half as a useful techniques in and information retrieved and and was the so-called scattered gathered by the cell start again there was billed as navigational interface way just were navigating through information space
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
Very strictly into something so if something as but document it's not business and what I do is still documents and the Middle might be difficult I'm
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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
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Dramatist all what amount lasted the other ways kind splitting so called devices all top down fast ringside stop with 1 big clubs and then outlook for Computer nice to the by its so he is a fixed based in all and the distance between them they should be different Clusters sell The dividing to of basic waste ways of doing some when I disunities you have some stability of the sums some of some measure of summit between the different Clustering faster than a simple at glamour to algorithm would be on the up for each document you create its own last stop with as many clubs area of human
And now for every 10 of documents you compute Howell because of the Miller between those documents all what is the distance between the documents and the 1 with the highest celebrity or below Systems between them they they'd get Murch together to for a single just look at So basically Computing Simler to between every payoff classes and very strenuous but you can improve on the political stole what you get is basically was called at dendrogram
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
Aggression left No question some get thanks fursuiting
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Computerunterstütztes Verfahren
Extrempunkt
Raum-Zeit
Computeranimation
Metropolitan area network
Datensatz
Iteration
Algorithmus
Mittelwert
Vorzeichen <Mathematik>
Schätzung
Minimum
Weitverkehrsnetz
Skript <Programm>
Abstand
Cluster <Rechnernetz>
Binärdaten
Physikalischer Effekt
Mathematisierung
Elektronischer Datenaustausch
Zeiger <Informatik>
Quick-Sort
Office-Paket
Abstand
Zahlenbereich
Konditionszahl
Heegaard-Zerlegung
Klumpenstichprobe
Personal Area Network
Fehlermeldung
Zeichenkette
Zentralisator
Partitionsfunktion
Subtraktion
Mereologie
Punkt
Virtualisierung
Extrempunkt
Selbstrepräsentation
Mathematisierung
Gewichtete Summe
Gruppenkeim
Iteration
Maßerweiterung
Computer
Extrempunkt
Ranking
Ähnlichkeitsgeometrie
Computeranimation
Open Source
Metropolitan area network
Informationsmodellierung
Iteration
Bit
Mittelwert
Gruppe <Mathematik>
Maximum-Likelihood-Schätzung
Weitverkehrsnetz
Punkt
Warteschlange
Maßerweiterung
Cluster <Rechnernetz>
Figurierte Zahl
Binärdaten
Diskrete Wahrscheinlichkeitsverteilung
Trennungsaxiom
Zentrische Streckung
Videospiel
Physikalischer Effekt
Güte der Anpassung
Ruhmasse
Objektklasse
Stichprobenumfang
Objekt <Kategorie>
Arithmetisches Mittel
Datenstruktur
Menge
Klumpenstichprobe
Personal Area Network
Softwaretest
Dualitätstheorie
Punkt
Gewichtete Summe
Iteration
Vorzeichen <Mathematik>
Computerunterstütztes Verfahren
Raum-Zeit
Computeranimation
Metropolitan area network
Informationsmodellierung
Diagramm
Menge
Spieltheorie
Programmbibliothek
Mehrrechnersystem
Sigma-Algebra
Personal Area Network
Gerade
Metropolitan area network
Dualitätstheorie
Bit
Punkt
Einheit <Mathematik>
Rechter Winkel
Klasse <Mathematik>
Weitverkehrsnetz
Mehrrechnersystem
Iteration
Code
Computeranimation
Abstimmung <Frequenz>
Subtraktion
Krümmung
Klasse <Mathematik>
Gewichtete Summe
Zahlenbereich
Befehl <Informatik>
Zählen
Term
Whiteboard
Computeranimation
Heegaard-Zerlegung
Übergang
Netzwerktopologie
Metropolitan area network
Informationsmodellierung
Minimum
Weitverkehrsnetz
Skript <Programm>
Cluster <Rechnernetz>
Chi-Quadrat-Verteilung
Normalvektor
Addition
Adressierung
Viereck
Vererbungshierarchie
Teilbarkeit
Quick-Sort
Rechter Winkel
Minimum
Information
Klumpenstichprobe
Aggregatzustand
Lesen <Datenverarbeitung>
Stabilitätstheorie <Logik>
Krümmung
Gewichtete Summe
Klasse <Mathematik>
Gewichtete Summe
Matrizenrechnung
Befehl <Informatik>
Computerunterstütztes Verfahren
Computer
Ähnlichkeitsgeometrie
Computeranimation
Heegaard-Zerlegung
Metropolitan area network
Bit
Abstand
Cluster <Rechnernetz>
Einflussgröße
Inklusion <Mathematik>
Algorithmus
Physikalisches System
Flächeninhalt
Heegaard-Zerlegung
Minimum
Messprozess
Klumpenstichprobe
Personal Area Network
Mittelwert
Punkt
t-Test
Gruppenkeim
Gewichtete Summe
Oval
Ähnlichkeitsgeometrie
Computeranimation
Übergang
Richtung
Netzwerktopologie
Trigonometrische Funktion
Einheit <Mathematik>
Weitverkehrsnetz
Wurzel <Mathematik>
Einflussgröße
Gerade
Funktion <Mathematik>
Physikalischer Effekt
Güte der Anpassung
Übergang
Bitrate
Speicherbereichsnetzwerk
Endlicher Graph
Gruppenkeim
Rechter Winkel
Verschlingung
Heegaard-Zerlegung
Ablöseblase
Messprozess
Simulation
Zeichenkette
Subtraktion
Klasse <Mathematik>
Zahlenbereich
Term
Hypermedia
Diskrete Simulation
Endlicher Graph
Datentyp
Cluster <Rechnernetz>
Videospiel
Binärcode
Finite-Elemente-Methode
Verzweigendes Programm
Fokalpunkt
Abstand
Klumpenstichprobe
Personal Area Network
Mittelwert
Distributionstheorie
TVD-Verfahren
Einfügungsdämpfung
Programmiergerät
Krümmung
Punkt
Extrempunkt
Nebenbedingung
Gewichtete Summe
Gruppenkeim
Computerunterstütztes Verfahren
Sondierung
Zählen
Lie-Gruppe
Ähnlichkeitsgeometrie
Raum-Zeit
Computeranimation
Internetworking
Metropolitan area network
Negative Zahl
Algorithmus
Ausreißer <Statistik>
Gruppe <Mathematik>
Mixed Reality
Lambda-Kalkül
Einflussgröße
Kette <Mathematik>
Metropolitan area network
Inklusion <Mathematik>
Softwaretest
Schwellwertverfahren
Ruhmasse
Ähnlichkeitsgeometrie
Bildschirmsymbol
Arithmetisches Mittel
Gruppenkeim
Menge
Einheit <Mathematik>
Verschlingung
Heegaard-Zerlegung
Wärmeleitfähigkeit
Tabelle <Informatik>
Zeichenkette
Nebenbedingung
Subtraktion
Total <Mathematik>
Wellenpaket
Mathematisierung
Klasse <Mathematik>
Gruppenoperation
Abgeschlossene Menge
Zahlenbereich
Zellularer Automat
Term
Heegaard-Zerlegung
Task
Mittelwert
Diskrete Simulation
Inhalt <Mathematik>
Abstand
Cluster <Rechnernetz>
Datenstruktur
Hilfesystem
Schreib-Lese-Kopf
Hierarchie <Mathematik>
Algorithmus
Videospiel
Paarvergleich
Last
Mereologie
Wort <Informatik>
Klumpenstichprobe
Bit
Leistungsbewertung
Total <Mathematik>
Gewichtete Summe
Indexberechnung
Negative Zahl
Klumpenstichprobe
Personal Area Network
Computeranimation
Relevanz-Feedback
Computeranimation

Metadaten

Formale Metadaten

Titel Document clustering (25.5.2011)
Serientitel Information Retrieval and Web Search Engines (SS 2011)
Teil 7
Anzahl der Teile 13
Autor Balke, Wolf-Tilo
Mitwirkende Selke, Joachim
Lizenz CC-Namensnennung - keine kommerzielle Nutzung 3.0 Deutschland:
Sie dürfen das Werk bzw. den Inhalt zu jedem legalen und nicht-kommerziellen Zweck nutzen, verändern und in unveränderter oder veränderter Form vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen.
DOI 10.5446/360
Herausgeber Technische Universität Braunschweig, Institut für Informationssysteme
Erscheinungsjahr 2011
Sprache Englisch
Produzent Technische Universität Braunschweig
Institut für Informationssysteme
Balke, Wolf-Tilo
Produktionsjahr 2011
Produktionsort Braunschweig

Technische Metadaten

Dateigröße 151 MB
Dauer 2:18:36

Inhaltliche Metadaten

Fachgebiet Informatik
Abstract 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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