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Spring 2015 Plan

Our focus this term is to establish a development platform and datasets on pTree1 for data mining. We aim to understand Treeminer ingest facilities and make advances in vertical data mining.

edmundswanson
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Spring 2015 Plan

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  1. Spring 2015 Plan The main focus of our work this term is to get a development platform and datasets (and Matt Piehl’s datasets) on pTree1 for all to use. Understand Treeminer ingest facilities so we can quickly and easily bring in new datasets and pTree-ize them. Given that vertical data mining is so new and pTree vertical data mining is even newer, any advance you see and can make as you get up to speed on his wonderful ingest software (includng ingesting into Hadoop) will be a paper/thesis.. Ingest is extremely important for 2 reasons; one cannot do pTree dataming without the dataset in pTree form; almost all capture data is immediately put in legacy horizontal form (even if it is capture in vertical form) so the cost of converting from horizontal form to vertical form (although a 1 time cost) is ours to bear for a while anyway. Some day we may convice some key people to stop the brainless step of immediately converting to horizontal or start the intelligent step of giving us the raw vertical data or???? We will try to get the system up and going (with Nate, Bryan and Dr. Wettstein’s help and advice) as soon as possible (let’s target Jan 12, 2015 as semester start). Where does that effort stand now? I will continue to present new ideas (here will be several student presentations of those taking 879, 765 or 799 also. I always invite any student taking a course from me (or any other student interested) to join us. The only requirement at this point in time is to sign an NDA for Treeminer if the student wishes access to that fantastic devel env. I would like each student who attends to pick a Vertical Data Mining with pTrees (VDMp), use your own knowledge and all the notes since 2010 (at my web site in the “secret” location  ) to produce a new version, enhanced and up-to-date version. You can pick a topic on a first come first serve basis. You can embed audio lectures if you wish (I recommend using Powerpoint 2003’s embedded audio feature so that over time, no user has to have access to, use or become familiar with sophisticated presentation software. I’m well aware that there are some very sophisticated online lecture environments, but my experience is that easy access for students trumps everything. Always open to a pitch for another platform, but it will take quite a good sales pitch ;-) ). The VDMp powerpoint enhancement can be accompanied with a set of problem assignments (with separated solutions), it can be on a new topic beyond the topics found on my web site, it can be accompanied by a test bank of questions and answers, it can be enhanced and updated from my Saturday notes and definitely also, from the recent data mining literature and books. If you are registered for one of my courses (or wish to be – let me know – we can do an individual study even), the VDMp topic enhancement can constitute your paper (notice I said “can be” but the paper can also be a traditional new killer idea of yours too). If you are registered for 879 maybe it should be a little more extensive like a book or two chapter on VDMp. The current (but poor) book in on the secret web site too. Let’s now go around an take out pulse. What is your situation this term wrt degree progress, topic choice, progress on your research toward degree, etc. Md: VDMp operations. Chapter in a book? It would become your thesis basis (and presentation). Try to think ahead of time what the optimal form would be. It should be able to fit right into our presentation, concept and terminology environment so that no preface has to be provided to get the reader used to a different terminology and style. Proposal defended already Rajat: MS CS, finished coursework, project: Software Quality Metrics (Dr. Magel) api to implement more (quality metrics to guide). Maninder: Ph.D. CS, (book needs structure) Plan: develop in Treeminer env., Spoorthy: MSE (no thesis) going to change to MS in SE, Damian: Ph.D. in SE, (Dr. Nygard advisor) Arjun: Ph.D. CS. (VDMp algorithms, 2 tasks s15, proposal this term, publish journal paper, Arijit: Ph.D. in CS, VDMp data mining of fanacials, proposal defense this term Bryan: Multilevel pTrees WP: summarized pTree matrix multiplication into a topics slideset., Graduate students are the best buy on the face of the earth The synergism between a smart advisor and a good student is the most productive synergism on the face of the earth, for the student, for the advisor and for the world.

  2. FAUST Analytics X=(X1..Xn)Rn, |X|=N. XC=(X1..Xn,C}. x.C{C1..Cc}=the_classes, is a classification TrainingSet. d=(d1..dn), p=(p1..pn)Rn. In FAUST we work with Functionals, F:RnR, F=L, S, R : PTSSPTS. Ld,p (X-p)od = Xod – pod Ld  Xod Sp  (X-p)o(X-p) = XoX + Xo(-2p) + pop = L-2p + XoX + pop Rd,p Sp - L2d,p = XoX+L-2p+pop-(Ld)2-2pod*Xod+(pod)d2 = L-2p-(2pod)d - (Ld)2+ XoX + pop+(pod)2 FAUST Hull Classifying An unclassified yCkiffyHullk {z | minFCk-  F(z)  maxFCk+} for as many Fs as possible. ( depends on the thoroughness of the TrainingSet. We call minFCk- and maxFCk+ the Hull Cut Points (HCPs). HCPs at precipitous count changes in F, give multiple hulls per class, facilitating recursive hulling of non-convex classes. FAUST Hull Classifying can be preceded by an attribute selection step. FAUST ClusteringStarting with 1 cluster C=X, and until a stop condition (e.g., cluster density > threshold), recursively cut C at each F-gap (e.g., at the midpoint or adjusted based on, e.g., subcluster variance) using a different F in each recursion step. PrecipitousCountChange gaps can be used instead of Value gaps to deal with suspected aberrations. FAUST Top K OutlierDetectorUse rankn-1Sx Mark 11/25 5PM FYI, some updated results in text classification. FAUST in and of itself is capable of accuracy as high as anything out there. Using Stanford_newsgroup dataset (7,500 docs).  plain Jane FAUST got 80% boost by eliminating terms that appears in <= 2 TrainingSet docs and by using chi-squared to reduce attributes a further 20% (pick 80% most important attr.). Vertical data allows us to toss attributes easily before we expend a lot of CPU. Tossing intelligently, we improve accuracy and reduce classification time!  We eliminated ~70% of the attribs from TestSet and achieved better accuracy than the classifiers referenced on Stanford Natural Language Processing site! We’re exploring other approaches to further identify the critical attribute. We are about to turn this loose on datasets approaching 1TB in size. Mark 11/26 9am We’re adjusting midpoint as well based on cluster deviation. This gives us an extra 4 percent or so accuracy over straight midpoint. The hull is interesting case, as we are looking at situations like this. We are already able to predict which members are poor matches to a class. Mark11/26 Related: dominant attributes may exist in only some classes. We must be factored in when ascribing weight/value to an attribute.

  3. Text Mining Comments A text corpus (d documents (rows) and t vocabulary terms (columns) we have so far recorded only a very tiny part of the total information contained in the corpus in our corpus matrix, which in its simplest form simply contains a 1 iff that term occurs at least once in that document. The term_frequency matrix would record the number of times that term occurs in that document. The tfidf matrix would record the term_frequency divided by the document frequency of that term. There is a wealth of other information in a text corpus, most of which is not captured by data miners and therefore not mined. I’d guess that’s what motivated Maninder to suggest considering documents one sentence at a time (sentence structure may hold lots of useful information to data miners?)? I looked back through the 2010-2015 notes and found where we had tried to capture more information than just term existence or weighted term frequency type information (check out the notes in and around 2012_07 to 2012_09). In that work we tried to capture the entire word sequence within each document for mining. We gave up on that because it got just too unwieldy, even when we used leveled pTrees to reduce the size. If you have an interest in making another run at capturing (and then mining information from) more of the corpus meaning, read those notes first, research what others have done or attempted to do and then move the frontiers of that body of work forward with a new killer idea. However, there are many derived pTrees in the middle ground that might hold lots of useful information for the text miner and which are easy to provide, given the vertical structuring. For example, see In these slides. EndofSentence Refrncs pTree Verb pTree LastChpt pTree Preface pTree For example, from the EndofSentence pTree (which records the document position of all the periods, question marks and etc, that end sentences in the document. Application? If two students turned in term papers with exactly the same EndofSentence pTrees, what might you conclude (or, being more careful, “How might you use this as decision support?” – remember never recommend taking the human thinker out of the loop. What we do is support their decisions. We never recommend allowing our software to make decisions!). Drilling down a bit – suppose we create the pTreeSet of the sequence of sentence lengths. (just the subtract consecutive EndofSentence positions). And then suppose we just calculate a correlation value between those pTreeSets for two students. If that correlation is very high, we look into it!

  4. level-1 TermFreqPTrees (E.g., the predicate of tfP0: mod(sum(mdl-stride),2)=1) <--dfP0 ... Term (Vocab) ..doc freq <--dfP2 0 0 1 1 2 0 0 0 0 0 df (cnt) 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 3 1 1 1 8 2 0 1 0 2 1 1 8 1 1 2 3 8 1 1 3 0 0 1 1 0 0 0 1 0 0 1 0 1 1 0 0 1 3 3 0 3 0 . . . 0 0 0 0 0 . . . 0 . . . 0 . . . 0 . . . ... tf0 0 . . . ... tf1 0 . . . ... tf 0 . . . 0 . . . 0 0 0 0 0 d=3 d=3 d=3 t=a t=again t=all ... doc=3 doc=3 doc=3 term=a trm=again term=all ... doc=1 d=1 d=1 term=a t=again t=all doc=1 doc=1 doc=1 term=a trm=again term=all doc=2 doc=2 doc=2 term=a trm=again term=all d=2 d=2 d=2 t=a t=again t=all 0 0 0 0 0 0 0 0 0 0 5 6 ...doc ...Term Freq ... tf2 ... tf0 ... tf1 ... Term Ex 1 0 0 JSE 0 0 1 0 . . . HHS LMM 0 0 0 0 0 0 0 . . . 0 0 0 0 0 0 0 . . . a apple 1 3 always. 1 1 1 1 April an again all and 1 2 are 3 7 1 2 3 4 Length of this level-1 TermExistencePTree =VocabLen*DocCount pred is NOTpure0 Length of this level-0 pTree= mdl*VocabLen*DocCount . . . 0 0 0 1 2 3 4 5 6 7 mdl reading-positions for doc=1, term=a (mdl = max doc length) 1 2 3 4 5 6 7 mdl reading-positions: doc=1, term=again 1 2 3 4 5 6 7 mdl reading-positions for doc=1, term=all DocTrmPos pTreeSet dfk isn't a level-2 pTree since it's not a predicate on level-1 te strides. Next slides shows how to do it differently so that even the dfk's come out as level-2 pTrees. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 . . . 0 0 0 0 0 0 0 . . . 0 0 0 0 0 0 0 . . . pTree Text Mining (from 2012_08_04 Data Cube Text Mining ... Position

  5. level-2 PTree, hdfP?? (Hi Doc Feq): pred=NOTpure0 applied to tfP1 <--dfP0 Vocab Terms ..doc freq hdfP <--dfP3 1 0 2 0 1 doc1 doc2 doc3 0 0 0 0 0 df count 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 doc1 doc2 doc3 1 0 1 1 3 0 1 1 1 0 0 0 0 8 8 0 2 1 8 1 0 1 0 1 1 0 1 0 0 0 . . . 0 0 . . . 2 . . . 0 . . . 3 3 3 . . . . . . ... tfP1 . . . ... tf 0 . . . . . . ... tfP0 . . . 0 . . . . . . 0 0 0 0 0 tePt=all d=1 d=2 d=3 t=all t=all t=all ... doc=1 d=2 d=3 term=a t=a t=a d=1 d=2 d=3 t=again t=again t=again 0 0 0 0 0 0 0 0 0 0 tePt=again tePt=a tr=all t=all t=all doc1 doc2 doc3 ... t=again t=again t=again doc1 doc2 doc3 trm=a trm=a term=a doc1 doc2 doc3 5 6 ...doc ... tf2 ... tf0 ...Term Freq ... tf1 ... Term Ex 0 0 0 JSE 0 0 1 0 . . . HHS LMM 0 0 0 0 0 0 0 . . . 0 0 0 0 0 0 0 . . . 1 always. 1 apple a 1 again 1 an 1 April 1 3 are 1 2 and all 7 1 2 3 4 These level-2 pTrees, dfPk have len= VocabLength level-1 PTrees, tfPk e.g., pred of tfP0: mod(sum(mdl-stride),2)=1 This one, overall, level-1 pTree, teP, has length = DocCount*VocabLength term=a doc2 term=a doc3 term=a doc1 term=again doc1 ... This one, overall, level-0 pTree, corpusP, has length = MaxDocLen*DocCount*VocabLen Corpus pTreeSet 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 . . . 0 0 0 0 0 0 0 . . . 0 0 0 0 0 0 0 . . . pTree Text Mining data Cube layout: ... Pos

  6. level-2 PTree, hdfP?? (Hi Doc Feq): pred=NOTpure0 applied to tfP1 <--dfP0 Vocab Terms ..doc freq hdfP <--dfP3 1 2 1 0 0 doc1 doc2 doc3 0 0 0 0 0 df count 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 1 3 1 0 1 2 0 1 0 0 0 0 1 1 1 8 8 8 0 0 0 0 0 1 1 1 0 1 1 0 . . . 3 0 3 2 . . . . . . ... tfP0 . . . ... tf 3 0 . . . 0 . . . . . . ... tfP1 . . . . . . . . . 0 . . . 2 . . . 0 0 0 0 0 0 0 0 0 0 This overall, level-1 pTree, teP, has length = DocCount*VocabLength 0 0 0 0 0 tePt=again tePt=all tePt=a tr=all t=all t=all doc1 doc2 doc3 ... t=again t=again t=again doc1 doc2 doc3 trm=a trm=a term=a doc1 doc2 doc3 0 0 0 1 0 1 1 0 1 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 1 0 0 1 1 0 0 Pt=a,d=3 0 Pt=a,d=2 5 6 Verb pTree Refrncs pTree EndofSentence Preface pTree LastChpt pTree ...doc ... tf1 ... tf2 ... tf ... tf0 ... te 0 0 0 JSE 0 0 0 0 Pt=a,d=1. . . HHS LMM 0 0 0 0 0 0 0 Pt=again,d=1 0 0 0 0 0 0 0 . . . 1 1 1 a apple always. 1 1 April 1 again an all 2 and 3 1 are 7 1 2 3 4 These level-2 pTrees, dfPk have len= VocabLength doc1 doc2 doc3 level-1 PTrees, tfPk e.g., pred of tfP0: mod(sum(mdl-stride),2)=1 d=1 d=2 d=3 t=all t=all t=all ... doc=1 d=2 d=3 term=a t=a t=a d=1 d=2 d=3 t=again t=again t=again This overall level-0 pTree corpusP length MaxDocLen*DocCount*VocabLen term=again doc1 ... term=a doc3 term=a doc2 term=a doc1 Any of these masks can be ANDed into the Pt= , d= pTrees before they are concatenated as above (or repetitions of the mask can be ANDED after they are concatenated). 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 . . . 0 0 0 0 0 0 0 . . . 0 0 0 0 0 0 0 . . . pTree Text Mining data Cube layout: ... Pos

  7. APPENDIX I have put together a pBase of 75 Mother Goose Rhymes or Stories. Created a pBase of the 15 documents with  30 words (Universal Document Length, UDL) using as vocabulary, all white-space separated strings. Little Miss Muffet Lev1 (term freq/exist) Lev-0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20... pos 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 . . . 182 te tf tf1 tf0 VOCAB Little Miss Muffet sat on a tuffet eating 1 2 1 0 a 0 0 0 0 0 1 0 0 0 0 0 0 again. 0 0 0 0 0 0 0 0 0 0 0 0 all 0 0 0 0 0 0 0 0 0 0 0 0 always 0 0 0 0 0 0 0 0 0 0 0 0 an 0 0 0 0 0 0 0 0 1 3 1 1 and 0 0 0 0 0 0 0 0 0 0 0 0 apple 0 0 0 0 0 0 0 0 0 0 0 0 April 0 0 0 0 0 0 0 0 0 0 0 0 are 0 0 0 0 0 0 0 0 0 0 0 0 around 0 0 0 0 0 0 0 0 0 0 0 0 ashes, 0 0 0 0 0 0 0 0 0 0 0 0 away 0 0 0 0 0 0 0 0 0 0 0 0 away 0 0 0 0 0 0 0 0 1 1 0 1 away. 0 0 0 0 0 0 0 0 0 0 0 0 baby 0 0 0 0 0 0 0 0 0 0 0 0 baby. 0 0 0 0 0 0 0 0 0 0 0 0 bark! 0 0 0 0 0 0 0 0 0 0 0 0 beans 0 0 0 0 0 0 0 0 0 0 0 0 beat 0 0 0 0 0 0 0 0 0 0 0 0 bed, 0 0 0 0 0 0 0 0 0 0 0 0 Beggars 0 0 0 0 0 0 0 0 0 0 0 0 begins. 0 0 0 0 0 0 0 0 1 1 0 1 beside 0 0 0 0 0 0 0 0 0 0 0 0 between 0 0 0 0 0 0 0 0 . . . 0 0 0 0 your 0 0 0 0 0 0 0 0 of curds and whey. There came a big spider and sat down... 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Level-2 pTrees (document frequency) df3 df2 df1 df0 df VOCAB te04 te05 te08 te09 te27 te29 te34 1 0 0 0 8 a 1 1 0 1 0 0 0 0 0 0 1 1 again. 0 1 0 0 0 0 0 0 0 1 1 3 all 0 1 0 0 0 0 0 0 0 0 1 1 always 0 0 0 0 0 1 0 0 0 0 1 1 an 0 0 0 0 0 0 0 1 1 0 1 13 and 1 1 1 1 1 1 1 0 0 0 1 1 apple 0 0 0 0 0 0 0 0 0 0 1 1 April 0 0 0 0 0 0 0 0 0 0 1 1 are 0 0 0 0 0 0 0 0 0 0 1 1 around 0 0 0 0 0 0 0 0 0 0 1 1 ashes, 0 0 0 0 0 0 0 0 0 1 0 2 away 0 0 0 0 0 1 0 0 0 1 0 2 away 0 0 0 0 0 1 0 0 0 0 1 1 away. 1 0 0 0 0 0 0 0 0 0 1 1 baby 0 0 0 0 1 0 0 0 0 0 1 1 baby. 0 0 0 1 0 0 0 0 0 0 1 1 bark! 0 0 0 0 0 0 0 0 0 0 1 1 beans 0 0 0 0 0 0 1 0 0 0 1 1 beat 0 0 0 0 0 0 0 0 0 0 1 1 bed, 0 0 0 0 0 1 0 0 0 0 1 1 Beggars 0 0 0 0 0 0 0 0 0 0 1 1 begins. 0 0 0 0 0 0 0 0 0 0 1 1 beside 1 0 0 0 0 0 0 0 0 0 1 1 between 0 0 1 0 0 0 0 Humpty Dumpty Lev1 (term freq/exist) Lev-0 1 2 3 4 5 6 7 8... pos 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 . . . 182 te tf tf1 tf0 05HDS Humpty Dumpty sat on a wall. Humpt yDumpty 1 2 1 0 a 0 0 0 0 1 0 0 0 1 1 0 1 again. 0 0 0 0 0 0 0 0 1 2 1 0 all 0 0 0 0 0 0 0 0 0 0 0 0 always 0 0 0 0 0 0 0 0 0 0 0 0 an 0 0 0 0 0 0 0 0 1 1 0 1 and 0 0 0 0 0 0 0 0 0 0 0 0 apple 0 0 0 0 0 0 0 0 0 0 0 0 April 0 0 0 0 0 0 0 0 0 0 0 0 are 0 0 0 0 0 0 0 0 0 0 0 0 around 0 0 0 0 0 0 0 0 0 0 0 0 ashes, 0 0 0 0 0 0 0 0 0 0 0 0 away 0 0 0 0 0 0 0 0 0 0 0 0 away 0 0 0 0 0 0 0 0 0 0 0 0 away. 0 0 0 0 0 0 0 0 0 0 0 0 baby 0 0 0 0 0 0 0 0 0 0 0 0 baby. 0 0 0 0 0 0 0 0 0 0 0 0 bark! 0 0 0 0 0 0 0 0 0 0 0 0 beans 0 0 0 0 0 0 0 0 0 0 0 0 beat 0 0 0 0 0 0 0 0 0 0 0 0 bed, 0 0 0 0 0 0 0 0 0 0 0 0 Beggars 0 0 0 0 0 0 0 0 0 0 0 0 begins. 0 0 0 0 0 0 0 0 0 0 0 0 beside 0 0 0 0 0 0 0 0 0 0 0 0 between 0 0 0 0 0 0 0 0 . . . 0 0 0 0 your 0 0 0 0 0 0 0 0

  8. FAUST Clustering1 L-GapClustererCut, C, mid-gap (of F&C) using next (d,p) from dpSet, where F=L|S|R 2-1 separates 7,50 2-2 separates.27s 2^?1 0 -1 -2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.27 0 0 0 1 0.27 0 0 0 1 0.27 0 0 0 1 0.27 0 0 0 1 0.27 0 0 0 1 0.27 0 0 0 1 0.27 0 0 0 1 0.27 0 0 0 1 0.27 0 0 0 1 0.27 0 0 0 1 0.27 0 0 0 1 0.27 0 0 0 1 0.27 0 0 0 1 0.27 0 0 0 1 0.27 0 0 0 1 0.27 0 0 0 1 0.27 0 0 0 1 0.55 0 0 1 0 0.55 0 0 1 0 3.60 1 1 1 0 D=d35 0 d26 0 d1 0 d27 0 d3 0 d44 0 d16 0 d6 0 d17 0 d47 0 d18 0 d10 0 d43 0 d12 0 d33 0 d14 0 d23 0 d49 0 d25 0 d45 0 d2 0 d29 0 d13 0 d9 0 d32 0.27 d28 0.27 d41 0.27 d42 0.27 d30 0.27 d21 0.27 d22 0.27 d15 0.27 d36 0.27 d11 0.27 d38 0.27 d46 0.27 d5 0.27 d8 0.27 d37 0.27 d48 0.27 d39 0.27 d4 0.55 d50 0.55 d7 3.60 d35 35, 7, 50 outliers D=.27s 0 d9 0 d49 0 d45 0.09 d6 0.09 d3 0.09 d33 0.09 d18 0.09 d44 0.18 d43 0.18 d25 0.18 d22 0.18 d12 0.18 d16 0.18 d2 0.27 d27 0.27 d23 0.27 d42 0.27 d15 0.27 d13 0.27 d47 0.36 d26 0.36 d29 0.36 d36 0.46 d38 0.46 d14 0.46 d48 0.46 d8 0.46 d10 0.46 d37 0.55 d32 0.55 d1 0.55 d5 0.64 d21 0.64 d4 0.64 d11 0.64 d17 0.92 d30 1.01 d41 1.01 d28 1.10 d39 1.29 d46 {28,30,39,41,46} Cluster D=.64s 0 d26 0 d33 0 d3 0 d27 0 d45 0 d2 0 d44 0 d23 0 d9 0 d15 0 d49 0 d16 0 d38 0 d6 0 d18 0 d22 0.25 d1 0.25 d37 0.25 d43 0.25 d8 0.25 d29 0.25 d25 0.25 d42 0.25 d12 0.25 d47 0.25 d48 0.51 d32 0.51 d14 0.51 d4 0.51 d36 0.51 d13 0.51 d5 0.77 d10 1.03 d11 1.29 d17 1.54 d21 the 0's, .25s, .51s are clusters. d10, d11, d17, d21 outliers Going back to D=d35, how close does HOB come? 21, 20 separate 35 C1 (.17  xod  .25)={2,3,6,16,18,22,42,43,49} D=sum of all C31docs 0.63 d17 0.63 d29 0.63 d11 0.84 d50 0.84 d13 0.84 d30 0.95 d26 0.95 d28 0.95 d10 0.95 d41 1.16 d21 C311(..63) ={11,17,29} C312(.84) ={13,30,50} C313(.95) ={10,26,28,41} 21 outlier C2 (.34  xod  .56)={1,4,5,8,9,12,14,15,23,25,27,32,33,36,37,38,44,45,47,48} C3 (.64xod.86)={10,11,13,17,21,26,28,29,30,39,41,50} Single: 46 (xod=.99); 7 (=1.16); 35 (=1.47) D=sum of allC2docs 0.27 d23 0.36 d25 0.36 d4 0.36 d38 0.45 d15 0.45 d33 0.45 d12 0.45 d36 0.54 d8 0.54 d44 0.54 d47 0.63 d1 0.63 d37 0.63 d5 0.63 d32 0.63 d50 0.72 d27 0.72 d45 0.72 d9 0.81 d14 Next, on each Ck try D=Ck, Thres=.2 D=sum of all C1docs 0.42 d16 0.42 d2 0.42 d3 0.42 d42 0.42 d43 0.42 d22 0.63 d18 0.63 d49 0.85 d6 C11(xod=.42)={231622,42,43} 6,18,49 outliers D=sum of all C11docs 0.57 d2 0.57 d3 0.57 d16 0.57 d22 0.57 d42 0.57 d43 D=sum of all C3docs 0.56 d11 0.66 d17 0.66 d29 0.75 d13 0.85 d30 0.85 d10 0.94 d28 0.94 d26 0.94 d41 0.94 d50 1.03 d21 1.41 d39 C31(.56xod1.03) ={10,11,13,17,21,26,28,29,30,41,50} 39 outlier Other Clustering methods later D=44docs GT=.08 0.17 d22 0.17 d49 0.21 d42 0.21 d2 0.21 d16 0.25 d18 0.25 d3 0.25 d43 0.25 d6 0.34 d23 0.34 d15 0.34 d44 0.34 d38 0.34 d25 0.34 d36 0.38 d33 0.38 d48 0.38 d8 0.43 d4 0.43 d12 0.47 d47 0.47 d9 0.47 d37 0.51 d5 0.56 d1 0.56 d32 0.56 d45 0.56 d14 0.56 d27 0.64 d10 0.64 d17 0.64 d21 0.64 d29 0.64 d11 0.69 d26 0.69 d50 0.69 d13 0.73 d30 0.77 d28 0.82 d41 0.86 d39 0.99 d46 1.16 d7 1.47 d35 C11: 2. This little pig went to market. This little pig stayed at home. This little pig had roast beef. This little pig had none. This little pig said Wee, wee. I can't find my way home. 3. Diddle diddle dumpling, my son John. Went to bed with his breeches on, one stocking off, and one stocking on. Diddle diddle dumpling, my son John. 16. Flour of England, fruit of Spain, met together in a shower of rain. Put in a bag tied round with a string. If you'll tell me this riddle, I will give you a ring. 22. Had a little husband no bigger than my thumb. I put him in a pint pot, and there I bid him drum. I bought a little handkerchief to wipe his little nose and a little garters to tie his little hose. 42. Bat bat, come under my hat and I will give you a slice of bacon. And when I bake I will give you a cake, if I am not mistaken. 43. Hark hark, the dogs do bark! Beggars are coming to town. Some in jags and some in rags and some in velvet gowns. C2: 1. Three blind mice! See how they run! They all ran after the farmer's wife, who cut off their tails with a carving knife. Did you ever see such a thing in your life as three blind mice? 4. Little Miss Muffet sat on a tuffet, eating of curds and whey. There came a big spider and sat down beside her and frightened Miss Muffet away. 5. Humpty Dumpty sat on a wall. Humpty Dumpty had a great fall. All the Kings horses, and all the Kings men cannot put Humpty Dumpty together again. 8. Jack Sprat could eat no fat. His wife could eat no lean. And so between them both they licked the platter clean. 9. Hush baby. Daddy is near. Mamma is a lady and that is very clear. 12. There came an old woman from France who taught grown-up children to dance. But they were so stiff she sent them home in a sniff. This sprightly old woman from France. 14. If all seas were one sea, what a great sea that would be! And if all the trees were one tree, what a great tree that would be! And if all the axes were one axe, what a great axe that would be! And if all the men were one man what a great man he would be! And if the great man took the great axe and cut down the great tree and let it fall into great sea, what a splish splash it would be! 15. Great A. little a. This is pancake day. Toss the ball high. Throw the ball low. Those that come after may sing heigh ho! 23. How many miles is it to Babylon? Three score miles and ten. Can I get there by candle light? Yes, and back again. If your heels are nimble and light, you may get there by candle light. 36. Little Tommy Tittlemouse lived in a little house. He caught fishes in other mens ditches. 37. Here we go round mulberry bush, mulberry bush, mulberry bush. Here we go round mulberry bush, on a cold and frosty morning. This is way we wash our hands, wash our hands, wash our hands. This is way we wash our hands, on a cold and frosty morning. This is way we wash our clothes, wash clothes, wash our clothes. This is way we wash our clothes, on a cold and frosty morning. This is way we go to school, go to school, go to school. This is the way we go to school, on a cold and frosty morning. This is the way we come out of school, come out of school, come out of school. This is the way we come out of school, on a cold and frosty morning. 38. If I had as much money as I could tell, I never would cry young lambs to sell. Young lambs to sell, young lambs to sell. I never would cry young lambs to sell. 44. The hart he loves the high wood. The hare she loves the hill. The Knight he loves his bright sword. The Lady loves her will. 47. Cocks crow in the morn to tell us to rise and he who lies late will never be wise. For early to bed and early to rise, is the way to be healthy and wealthy and wise. 48. One two, buckle my shoe. Three four, knock at the door. Five six, ick up sticks. Seven eight, lay them straight. Nine ten. a good fat hen. Eleven twelve, dig and delve. Thirteen fourteen, maids a courting. Fifteen sixteen, maids in the kitchen. Seventeen eighteen. maids a waiting. Nineteen twenty, my plate is empty. C311: 11. One misty moisty morning when cloudy was weather, I met an old man clothed all in leather. He began to compliment and I began to grin. How do And how do? And how do again 17. Here sits the Lord Mayor. Here sit his two men. Here sits the cock. Here sits the hen. Here sit the little chickens. Here they run in. Chin chopper, chin chopper, chin chopper, chin! 29. When little Fred went to bed, he always said his prayers. He kissed his mamma and then his papa, and straight away went upstairs. C312: 13. A robin and a robins son once went to town to buy a bun. They could not decide on plum or plain. And so they went back home again. 30. Hey diddle diddle! The cat and the fiddle. The cow jumped over the moon. The little dog laughed to see such sport, and the dish ran away with the spoon. 50. Little Jack Horner sat in the corner, eating of Christmas pie. He put in his thumb and pulled out a plum and said What a good boy am I! C313: 10. Jack and Jill went up the hill to fetch a pail of water. Jack fell down, and broke his crown and Jill came tumbling after. When up Jack got and off did trot as fast as he could caper, to old Dame Dob who patched his nob with vinegar and brown paper. 26. Sleep baby sleep. Our cottage valley is deep. The little lamb is on the green with woolly fleece so soft and clean. Sleep baby sleep. Sleep baby sleep, down where the woodbines creep. Be always like the lamb so mild, a kind and sweet and gentle child. Sleep baby sleep. 28. Baa baa black sheep, have you any wool? Yes sir yes sir, three bags full. One for my master and one for my dame, but none for the little boy who cries in the lane. 41. Old King Cole was a merry old soul. And a merry old soul was he. He called for his pipe and he called for his bowl and he called for his fiddlers three. And every fiddler, he had a fine fiddle and a very fine fiddle had he. There is none so rare as can compare with King Cole and his fiddlers three.

  9. FAUST Cluster 1.2 OUTLIER: 46. Tom Tom the piper's son, stole a pig and away he run. The pig was eat and Tom was beat and Tom ran crying down the street. WS0= 2 3 13 20 22 25 38 42 44 49 50 DS1= | WS1= 2 20 25 46 49 51 46 | DS2 46 DS0=|WS1= 7 10 17 23 25 28 33 34 37 40 43 45 50 35 |---| |DS2| |35 | OUTLIER: 35. Sing a song of sixpence, a pocket full of rye. 4 and 20 blackbirds, baked in a pie. When the pie was opened, the birds began to sing. Was not that a dainty dish to set before the king? The king was in his counting house, counting out his money. Queen was in the parlor, eating bread and honey. The maid was in the garden, hanging out the clothes. When down came a blackbird and snapped off her nose. WS0= 2 3 13 32 38 42 44 52 DS1 |WS1= 42(Mother) 7 9 |DS2|WS2=WS1 11 |7 27 |9 27 29 45 29 32 29 41 45 C1: Mother theme 7. Old Mother Hubbard went to the cupboard to give her poor dog a bone. When she got there cupboard was bare and so the poor dog had none. She went to baker to buy him some bread. When she came back dog was dead. 9. Hush baby. Daddy is near. Mamma is a lady and that is very clear. 27. Cry baby cry. Put your finger in your eye and tell your mother it was not I. 29. When little Fred went to bed, he always said his prayers. He kissed his mamma and then his papa, and straight away went upstairs. 45. Bye baby bunting. Father has gone hunting. Mother has gone milking. Sister has gone silking. And brother has gone to buy a skin to wrap the baby bunting in. WS0 22 38 44 52 DS1 WS1= 27 38 44 {fiddle(32 41) man(11 32) old(11 44) 11 DS2 32 11 41 22 44 C2 fiddle old man theme 11. One misty moisty morning when cloudy was weather, I chanced to meet an old man clothed all in leather. He began to compliment and I began to grin. How do you do How do you do? How do you do again 32. Jack come and give me your fiddle, if ever you mean to thrive. No I will not give my fiddle to any man alive. If I'd give my fiddle they will think I've gone mad. For many a joyous day my fiddle and I have had 41. Old King Cole was a merry old soul. And a merry old soul was he. He called for his pipe and he called for his bowl and he called for his fiddlers three. And every fiddler, he had a fine fiddle and a very fine fiddle had he. There is none so rare as can compare with King Cole and his fiddlers three. DS0|WS1 2 9 12 18 19 21 26 27 30 32 38 39 42 44 45 47 49 52 54 55 57 60 1 |DS1| WS2 12 19 26 39 44 10 |10 | DS2| WS3 13 | | 10 | DS3 10 17 37 14 39 21 41 26 44 28 30 47 50 OUTLIER: 10. Jack and Jill went up hill to fetch a pail of water. Jack fell down, and broke his crown and Jill came tumbling after. When up Jack got and off did trot as fast as he could caper, to old Dame Dob who patched his nob with vinegar and brown paper. DS0| WS1=2 9 18 21 30 38 41 45 47 49 52 54 55 57 60 1 | DS1|WS2=2 9 18 30 39 45 55 13 | 39 |DS2 14 | |39 17 21 39 28 41 30 47 37 50 OUTLIER: 39. A little cock sparrow sat on a green tree. He chirped and chirped, so merry was he. A naughty boy with his bow and arrow, determined to shoot this little cock sparrow. This little cock sparrow shall make me a stew, and his giblets shall make me a little pie. Oh no, says the sparrow I will not make a stew. So he flapped his wings\,away he flew C3: men three 1. Three blind mice! See how they run! They all ran after the farmer's wife, who cut off their tails with a carving knife. Did you ever see such a thing in your life as three blind mice? 5. Humpty Dumpty sat on a wall. Humpty Dumpty had a great fall. All the Kings horses, and all the Kings men cannot put Humpty Dumpty together again. 14. If all the seas were one sea, what a great sea that would be! And if all the trees were one tree, what a great tree that would be! And if all the axes were one axe, what a great axe that would be! And if all the men were one man what a great man he would be! And if the great man took the great axe and cut down the great tree and let it fall into the great sea, what a splish splash that would be! 17. Here sits the Lord Mayor. Here sit his two men. Here sits the cock. Here sits the hen. Here sit the little chickens. Here they run in. Chin chopper, chin chopper, chin chopper, chin! 23. How many miles is it to Babylon? Three score miles and ten. Can I get there by candle light? Yes, and back again. If your heels are nimble and light, you may get there by candle light. 28. Baa baa black sheep, have you any wool? Yes sir yes sir, three bags full. One for my master and one for my dame, but none for the little boy who cries in the lane. 36. Little Tommy Tittlemouse lived in a little house. He caught fishes in other mens ditches. 48. One two, buckle my shoe. Three four, knock at the door. Five six, pick up sticks. Seven eight, lay them straight. Nine ten. a good fat hen. Eleven twelve, dig and delve. Thirteen fourteen, maids a courting. Fifteen sixteen, maids in the kitchen. Seventeen eighteen. maids a waiting. Nineteen twenty, my plate is empty. WS0 38 52 DS1 WS1= 38 52 1 ---------- 5 17 23 28 36 48 C4: 4. Little Miss Muffet sat on a tuffet, eating of curds and whey. There came a big spider and sat down beside her and frightened Miss Muffet away. 6. See a pin and pick it up. All the day you will have good luck. See a pin and let it lay. Bad luck you will have all the day. 8. Jack Sprat could eat no fat. Wife could eat no lean. Between them both they licked platter clean. 12. There came an old woman from France who taught grown-up children to dance. But they were so stiff she sent them home in a sniff. This sprightly old woman from France. 15. Great A. little a. This is pancake day. Toss the ball high. Throw the ball low. Those that come after may sing heigh ho! 18. I had two pigeons bright and gay. They flew from me the other day. What was the reason they did go? I can not tell, for I do not k 21. Lion and Unicorn were fighting for crown. Lion beat Unicorn all around town. Some gave them white bread and some gave them brown. Some gave them plum cake, and sent them out of town. 25. There was an old woman, and what do you think? She lived upon nothing but victuals, and drink. Victuals and drink were the chief of her diet, and yet this old woman could never be quiet. 26. Sleep baby sleep. Our cottage valley is deep.Little lamb is on green with woolly fleece so soft, clean. Sleep baby sleep. Sleep baby sleep, down where woodbines creep. Be always like lamb so mild, a kind and sweet and gentle child. Sleep baby sleep. 30. Hey diddle diddle! The cat and the fiddle. The cow jumped over the moon. The little dog laughed to see such sport, and the dish ran away with the spoon. 33. Buttons, a farthing a pair! Come, who will buy them of me? They are round and sound and pretty and fit for girls of the city. Come, who will buy them of me? Buttons, a farthing a pair! 37. Here we go round mulberry bush, mulberry bush, mulberry bush. Here we go round mulberry bush, on a cold and frosty morning. This is way we wash our hands, wash our hands, wash our hands. This is way we wash our hands, on a cold and frosty morning. This is way we wash our clothes, wash our clothes, wash our clothes. This is way we wash our clothes, on a cold and frosty morning. This is way we go to school, go to school, go to school. This is the way we go to school, on a cold and frosty morning. This is the way we come out of school, come out of school, come out of school. This is the way we come out of school, on a cold and frosty morning. 43. Hark hark, the dogs do bark! Beggars are coming to town. Some in jags and some in rags and some in velvet gowns. 44. The hart he loves the high wood. The hare she loves the hill. The Knight he loves his bright sword. The Lady loves her will. 47. Cocks crow in the morn to tell us to rise and he who lies late will never be wise. For early to bed and early to rise, is the way to be healthy and wealthy and wise. 49. There was a little girl who had a little curl right in the middle of her forehead. When she was good she was very very good and when she was bad she was horrid. 50. Little Jack Horner sat in the corner, eating of Christmas pie. He put in his thumb and pulled out a plum and said What a good boy am I! WS0=2 5 8 11 14 15 16 22 24 25 29 31 36 41 44 47 48 53 54 57 59 DS1|WS1(17wds)=2 5 11 15 16 22 24 25 29 31 41 44 47 48 54 57 59 4 6 8|DS2=DS1 12 15 18 21 25 26 30 33 37 43 44 47 49 50 DS0|WS1 2 5 8 13 14 15 16 22 24 25 29 36 41 44 47 48 51 54 57 59 13 |DS2|WS2 4 13 47 51 54 14 |13 |DS3 13 21 26 30 37 47 50 OUTLIER: 13. A robin and a robins son once went to town to buy a bun. They could not decide on plum or plain. And so they went back home again. OUTLIERS: 2. This little pig went to market. This little pig stayed home. This little pig had roast beef. This little pig had none. This little pig said Wee, wee. I can't find my way home 3. Diddle diddle dumpling, my son John. Went to bed with his breeches on, one stocking off, and one stocking on. Diddle diddle dumpling, my son John. 16. Flour of England, fruit of Spain, met together in a shower of rain. Put in a bag tied round with a string. If you'll tell me this riddle, I will give you a ring. 22. Had little husband no bigger than my thumb. Put him in a pint pot, there I bid him drum. Bought a little handkerchief to wipe his little nose, pair of little garters to tie little hose 42. Bat bat, come under my hat and I will give you a slice of bacon. And when I bake I will give you a cake, if I am not mistaken. DS0|WS1=6 7 8 14 43 46 48 51 53 57 2 3|DS2=DS1 16 22 42 Each of the 10 words occur in 1 doc, so all 5 docs are outliers real HOB Alternate WS0, DS0 OUTLIER:38. If I had as much money as I could tell, I never would cry young lambs to sell. Young lambs to sell, young lambs to sell. I never would cry young lambs to sell. Notes Using HOB, the final WordSet is the document cluster theme! When the theme is too long to be meaningful (C4) we can recurse on those (using the opposite DS)|WS0?). The other thing we can note is that DS) almost always gave us an outliers (except for C5) and only WS) almost always gave us clusters (excpt for the first one, 46). What happens if we reverse it? What happens if we just use WS0?

  10. real HOB Alternate WS0, DS0 recuring on C3 and C4 FAUST Cluster 1.2.1 DS0|WS1=41 47 57 (on C4) 21|DS2 WS2=41(morn) 57(way) 26| 37 DS3=DS2 30| 47 . 37 47 50 C4.1 37. Here we go round mulberry bush, mulberry bush, mulberry bush. Here we go round mulberry bush, on a cold and frosty morning. This is way we wash our hands, wash our hands, wash our hands. This is way we wash our hands, on a cold and frosty morning. This is way we wash our clothes, wash our clothes, wash our clothes. This is way we wash our clothes, on a cold and frosty morning. This is way we go to school, go to school, go to school. This is the way we go to school, on a cold and frosty morning. This is the way we come out of school, come out of school, come out of school. This is the way we come out of school, on a cold and frosty morning. 47. Cocks crow in the morn to tell us to rise and he who lies late will never be wise. For early to bed and early to rise, is the way to be healthy and wealthy and wise. WS0=2 5 11 15 16 22 24 25 29 31 44 47 54 59 DS1|WS1=2 5 15 16 22 24 25 44 47 54 59 4 DS2 WS2=2 15 16 24 25 44 47 54 59 6 4 DS3 WS3=WS2 8 6 4 12 8 8 15 12 12 18 21 21 21 25 25 25 26 26 26 30 30 30 43 43 43 50 50 49 50 DS0|WS1= 47 (plum) 21 DS2 WS2=WS1 26 21 30 50 50 C4.2.1 word47(plum) 21. Lion &Unicorn were fighting for crown. Lion beat Unicorn all around town. Some gave them white bread and some gave them brown. Some gave them plum cake sent them out of town. 50. Little Jack Horner sat in corner, eating of Christmas pie. He put in his thumb and pulled out a plum and said What a good boy am I! WS0= 2 15 16 23 24 27 36 DS1|WS1 = 2 15 16 25 44 59 4 |DS2 WS2=15 16 25 44 59 8 |4 DS3 WS3=15 16 44 59 12 |8 8 DS4 WS4=15 44 59 25 |12 12 12 DS5 WS544 59 26 |25 25 25 12 DS6=DS5 30 |26 26 26 25 C4.2.2 word44(old) word59(woman) 12. There came an old woman from France who taught grown-up children to dance. But they were so stiff she sent them home in a sniff. This sprightly old woman from France. 25. There was old woman. What do you think? She lived upon nothing but victuals, and drink. Victuals and drink were the chief of her diet, and yet this old woman could never be quiet. Final WordSet is too long. Recurse 4.2 OUTLIER: 6. See a pin and pick it up. All the day you will have good luck. See a pin and let it lay. Bad luck you will have all the day. WS0= 5 11 22 25 29 31 DS1 WS1=5 22 6 1518 49 DS2 6 C4.2.3 (day eat girl) 4. Little Miss Muffet sat on tuffet, eating curd, whey. Came big spider, sat down beside her, frightened Miss Muffet away 8. Jack Sprat could eat no fat. Wife could eat no lean. Between them both they licked platter clean. 15. Great A. little a. This is pancake day. Toss the ball high. Throw the ball low. Those that come after may sing heigh ho! 18. I had 2 pigeons bright and gay. They flew from me other day. What was the reason they did go? I can not tell, for I do not know. 33. Buttons, farthing pair! Come who will buy them? They are round, sound, pretty, fit for girls of city. Come, who will buy ? Buttons, farthing a pair 49. There was little girl had little curl right in the middle of her forehead. When she was good she was very good and when she was bad she was horrid. DS0|WS1=22 25 29 4 DS2 =WS1 8 |4 8 15|15 18Recursing 18|33 49 no change 33 43 49 DS0|WS1=1 2 3 15 16 23 24 27 30 36 49 60 26 |DS1=DS0 30 Doc26 and doc30 have none of the 12 words in commong so these two will come out outliers on the next recursion! OUTLIERS: 26. Sleep baby sleep. Cottage valley is deep.Little lamb is on green with woolly fleece soft, clean. Sleep baby sleep. Sleep baby sleep, down where woodbines creep. Be always like lamb so mild, a kind and sweet and gentle child. Sleep baby sleep. 30. Hey diddle diddle! Cat and the fiddle. Cow jumped over moon.Little dog laughed to see such sport, and dish ran away with spoon. DS0=|WS1=21 38 49 52 1 |DS1 |WS2=21 38 49 14 |1 |DS3=DS2 17 |14 28 |17 C31 [21]cut [38]men [49]run 1. Three blind mice! See how run! All ran after farmer's wife, cut off tails with carving knife. Ever see such thing in life as 3 blind mice? 14. If all seas were 1 sea, what a great sea that would be! And if all trees were 1 tree, what a great tree that would be! And if all axes were 1 axe, what a great axe that would be! if all men were 1 man what a great man he would be! And if great man took great axe and cut down great tree and let it fall into great sea, what a splish splash that would be! 17. Here sits Lord Mayor. Here sit his 2 men. Here sits the cock. Here sits hen. Here sit the little chickens. Here they run in. Chin chopper, chin chopper, chin chopper, chin! C32: [38]men [52] three 5. Humpty Dumpty sat on wall. Humpty Dumpty had great fall. All Kings horses, all Kings men cannot put Humpty Dumpty together again. 23. How many miles to Babylon? 3 score miles and 10. Can I get there by candle light? Yes, back again. If your heels are nimble, light, you may get there by candle light. 28. Baa baa black sheep, have you any wool? Yes sir yes sir, three bags full. One for my master and one for my dame, but none for the little boy who cries in the lane. 36. Little Tommy Tittlemouse lived in a little house. He caught fishes in other mens ditches. 48. One two, buckle my shoe. Three four, knock at the door. Five six, pick up sticks. Seven eight, lay them straight. Nine ten. a good fat hen. Eleven twelve, dig and delve. Thirteen fourteen, maids a courting. Fifteen sixteen, maids in the kitchen. Seventeen eighteen. maids a waiting. Nineteen twenty, my plate is empty. WS0=38 52 DS1|WS1=WS0 5 | . 23 28 36 48 Doc43 and doc44 have none of the 6 words in commong so these two will come out outliers on the next recursion! OUTLIERS: 43. Hark hark, the dogs do bark! Beggars are coming to town. Some in jags and some in rags and some in velvet gowns. 44. The hart he loves the high wood. The hare she loves the hill. The Knight he loves his bright sword. The Lady loves her will. recurse on C3:

  11. HOB Alternate WS0, DS0 FAUST Cluster 1.2.2 eat girl day men 33 4 15 5 49 18 36 8 32 men 11 fiddle old 41 1 run cut 17 men 14 28 three 23 three 48 three morn old 37 12 47 25 way woman 16 OUTLIERS: 2 3 6 10 13 16 22 26 30 35 38 39 42 43 44 46 Categorize clusters (hub-spoke, cyclic, chain, disjoint...)? Separate disjoint sub-clusters? Each of the 3 C423 words gives a disjoint cluster!Each of the 2 C32 work gives a disjoint sub-clusters also. C4231 day 15. Great A. little a. This is pancake day. Toss ball high. Throw ball low. Those come after sing heigh ho! 18. I had 2 pigeons bright and gay. They flew from me other day. What was reason they go? I can not tell, I do not know. C4232 eat 4. Little Miss Muffet sat on tuffet, eat curd, whey. Came big spider, sat down beside her, frightened away 8. Jack Sprat could eat no fat. Wife could eat no lean. Between them both they licked platter clean. C4233 girl 33. Buttons, farthing pair! Come who will buy them? They are round, sound, pretty, fit for girls of city. Come, who will buy ? Buttons, farthing a pair 49. There was little girl had little curl right in the middle of her forehead. When she was good she was very good and when she was bad she was horrid. C1: mother 7. Old Mother Hubbard went to cupboard to give her poor dog a bone. When she got there cupboard was bare, so poor dog had none. She went to baker to buy some bread. When she came back dog was dead. 9. Hush baby. Daddy is near. Mamma is a lady and that is very clear. 27. Cry baby cry. Put your finger in your eye and tell your mother it was not I. 29. When little Fred went to bed, he always said his prayers. He kissed his mamma and then his papa, and straight away went upstairs. 45. Bye baby bunting. Father has gone hunting. Mother has gone milking. Sister has gone silking. And brother has gone to buy a skin to wrap the baby bunting in. C2: fiddle old men {cyclic} 11. 1 misty moisty morning when cloudy was weather, Chanced to meet old man clothed all leather. He began to compliment,I began to grin. How do you do How do? How do again 32. Jack come give me your fiddle, if ever you mean to thrive. No I'll not give fiddle to any man alive. If I'd give my fiddle they will think I've gone mad. For many joyous day fiddle and I've had 41. Old King Cole was merry old soul. Merry old soul was he. He called for his pipe, he called for his bowl, he called for his fiddlers 3. And every fiddler, had a fine fiddle, a very fine fiddle had he. There is none so rare as can compare with King Cole and his fiddlers three. C11 cut men run {cyclic} 1. Three blind mice! See how run! All ran after farmer's wife, cut off tails with carving knife. Ever see such thing in life as 3 blind mice? 14. If all seas were 1 sea, what a great sea that would be! And if all trees were 1 tree, what a great tree that would be! And if all axes were 1 axe, what a great axe that would be! if all men were 1 man what a great man he would be! And if great man took great axe and cut down great tree and let it fall into great sea, what a splish splash that would be! 17. Here sits Lord Mayor. Here sit his 2 men. Here sits the cock. Here sits hen. Here sit the little chickens. Here they run in. Chin chopper, chin chopper, chin chopper, chin! C321 men 5. Humpty Dumpty sat on wall. Humpty Dumpty had great fall. All Kings horses, all Kings men can't put Humpty together again. 36. Little Tommy Tittlemouse lived in little house. He caught fishes in other mens ditches. C322 three 23. How many miles to Babylon? 3 score 10. Can I get there by candle light? Yes, back again. If your heels are nimble, light, you may get there by candle light. 28. Baa baa black sheep, have any wool? Yes sir yes sir, 3 bags full. One for my master and one for my dame, but none for the little boy who cries in the lane. 48. One two, buckle my shoe. Three four, knock at the door. Five six, pick up sticks. Seven eight, lay them straight. Nine ten. a good fat hen. Eleven twelve, dig and delve. Thirteen fourteen, maids a courting. Fifteen sixteen, maids in the kitchen. Seventeen eighteen. maids a waiting. Nineteen twenty, my plate is empty. C4.1 morn way 37. Here we go round mulberry bush, mulberry bush, mulberry bush. Here we go round mulberry bush, on cold and frosty morn. This is way wash our hands, wash our hands, wash our hands. This is way wash our hands, on a cold and frosty morning. This is way we wash our clothes, wash our clothes, wash our clothes. This is way we wash r clothes, on a cold and frosty morning. This is way we go to school, go to school, go to school. This is the way we go to school, on a cold and frosty morning. This is the way we come out of school, come out of school, come out of school. This is the way we come out of school, on a cold and frosty morning. 47. Cocks crow in the morn to tell us to rise and he who lies late will never be wise. For early to bed and early to rise, is the way to be healthy and wealthy and wise. C421 plum 21. Lion &Unicorn were fighting for crown. Lion beat Unicorn all around town. Some gave them white bread and some gave them brown. Some gave them plum cake sent them out of town. 50. Little Jack Horner sat in corner, eating of Christmas pie. He put in his thumb and pulled out a plum and said What a good boy am I! C422 old woman 12. There came an old woman from France who taught grown-up children to dance. But they were so stiff she sent them home in a sniff. This sprightly old woman from France. 25. There was old woman. What do you think? She lived upon nothing but victuals, and drink. Victuals and drink were the chief of her diet, and yet this old woman could never be quiet. Let's pause and ask "What are we after?" Of course it depends upon the client. 3 main categories for relatioinship mining? text corpuses, market baskets (includes recommenders), bioinformatics? Others? What do we want from text mining? (anomalies detection, cliques, bicliques?) What do we want from market basket mining? (future purchase predictions, recommendations...) What do we want in bioinformatics? (cliques, strong clusters, ...???)

  12. FAUST Cluster 1.2.3 run 1 30 three 1 cut two old plum cut 23 three 10 fall brown 5 14 21 33 33 21 48 48 men three fall crown buy 48 three girl girl 5 14 king buy plum town 28 old three bread old 32 bake men buy back back 7 13 42 6 23 23 49 49 men 11 fiddle tree maid bad 7 bake sing men men old 41 36 day bake bread house town run 1 36 35 15 43 run dog cloth run 37 17 cloth 11 11 hill cut 37 morn way 14 dog men day old nose morn way day 32 22 eat dish 32 three day high fiddle round old son plum thumb king fiddle 18 44 47 bright wife 47 2 bed cock way old 41 41 pig 3 8 8 41 son round men fiddle pie eat merry mother 17 three clean mother 17 bed run cock always child away 4 26 29 4 old woman 12 away away 30 39 25 away 46 mother 29 old woman 12 25 eat cry mother green baby pie eat boy 9 50 50 boy 28 28 16 bag 9 lamb cry money mother baby 27 mother lamb 38 cry 27 baby full mother mother 45 lady 45 eat men word-labeled document graph We have captured only a few of the salient sub-graphs. Can we capture more of them? Of course we can capture a sub-graph for each word, but that might be 100,000. Let's stare at what we got and try to see what we might wish we had gotten in addition. A bake-bread sub-corpus would have been strong. (docs{7 21 35 42) A bake-bread sub-corpus would have been strong. (docs{7 21 35 42) There are many others. Using AVG+1 2 9 10 25 45 47 d21 0 0 1 0 0 1 d35 0 0 1 1 1 0 d39 1 1 0 0 1 0 d46 1 0 0 1 0 0 d50 0 1 0 1 1 1

  13. HOB2 Alt (use other HOBs) FAUST Cluster 1.2.4 run 1 30 three cut two old 10 fall brown 5 14 21 33 21 48 men three fall crown buy girl king buy plum town old bread bread old bake buy back back 7 13 42 6 23 49 men tree maid bad bake sing men 36 day bake bread house town run 35 35 15 43 dog cloth 37 cloth 11 hill 35 morn way dog day old nose 32 22 eat dish three day high fiddle round old son plum plum thumb king old 18 44 47 bright wife 2 bed cock way 41 pig 3 8 son round men fiddle pie pie eat merry mother 17 pie three clean 12. There came an old woman from France who taught grown-up children to dance. But they were so stiff she sent them home in a sniff. This sprightly old woman from France. bed run eat cock always child away 26 29 4 old woman 39 12 away away 30 39 25 away away 46 46 eat child 26 mother old woman 12 39 eat eat eat 46 cry pie boy pie boy green eat baby boy pie baby c o w h l o i d m l a d n 15 44 59 d12 1 1 1 9 50 50 boy 28 16 bag 9 50 lamb cry money mother baby 27 baby lamb 38 cry 27 baby full mother 45 39 lady boy boy 50 boy 28 wAvg+1, dAvg+1 a b b e p p w o r a i l a y e t e u y a m d 2 9 10 25 45 47 d21 0 0 1 0 0 1 d35 0 0 1 1 1 0 d39 1 1 0 0 1 0 d46 1 0 0 1 0 0 d50 0 1 0 1 1 1 recurse: wAv+2,dAvg-1 e p a i t e 2 9 10 25 45 d35 0 0 1 1 1 d39 1 1 0 0 1 d46 1 0 0 1 0 d50 0 1 0 1 1 And if we want to pull out a particular word cluster, just turn the word-pTree into a list.: w=baby a b w a b a y 2 3 d9 0 1 d26 1 1 d27 0 1 d45 1 w=boy a b w o a y 2 9 d28 0 1 d39 1 1 d50 0 1 For a particular doc cluster, just turn the doc-pTree into a list:

  14. FAUST HULL Classification 1 Using the clustering of FAUST Clustering1 as classes, we extract 80% from each class as TrainingSet (w class=cluster#). How accurate is FAUST Hull Classification on the remaining 20% plus the outliers (which should be "Other"). C11={2,3,16,22,42,43} C2 ={1,4,5,8,9,12,14,15,23,25,27,32,33,36,37,38,44,45,47,48} C11={3} C11={2,16,22,42,43} C311= {11,17,29} C312={13,30,50} C313={10,26,28,41} C2 ={4,14,23,45} C2 ={1,5,8,9,12,15,25,27,32,33,36,37,38,44,47,48} Full classes from slide: FAUST Clustering1 20% Test Set C311= {11,17} C312={30,50} C313={10,28,41} C311= {29} C312={13} C313={26} 80% Training Set OUTLIERS {18,49} {6} {39} {21} {46} {7} {35} O={18 49 6 39 21 46 7 35} .305 .439 C312 D11=C11 p=avC11 L MIN MAX CLASS .63 .63 C11 0 .63 C2 0 0 C311 .31 .31 C312 0 .31 C313 0 C311 .31 C312 0 .22 C11 .44 .66 C313 D2=C2 p=avC2 L MIN MAX CLS 0 .22 C11 .44 .77 C2 .66 .66 C311 .11 .22 C312 .44 .66 C313 -.09 .106 C11 D1=TS p=avTS Lpd MIN MAX CLASS -0.09 .106 C11 0.106 .439 C2 0.572 .572 C311 0.305 .439 C312 0.505 .771 C313 .572 C311 .11 .22 C312 .44 .77 C2 0 .31 C313 .63 C11 .106 .439 C2 .505 .771 C313 .66 C311 0 .63 C2 0 C11 .31 C11 .31 C2 .31 C311 .31 C313 D312=C312 p=avC312 L MN MX CLAS 0 .31 C11 0 .31 C2 0 .31 C311 1.58 1.58 C312 0 .31 C313 0 .22 C11 0 .44 C2 0 .44 C311 D311=C311 p=avC311 L MN MX CLAS 0 0 C11 0 0.66 C2 1.33 1.66 C311 0 0.33 C312 0 0.33 C313 D313=C313 p=avC313 L MN MX CLAS 0 .22 C11 0 .44 C2 0 .44 C311 0.22 .22 C312 1.34 1.56 C313 1.3 1.6 C311 0 .33 C312 0 .33 C313 1.34 1.56 C313 .22 C312 1.58 C312 0 .66 C2 D1=TS p=avTS Sp 4.2 C313 5.4 1.9 C11 2.1 2.4 C311 3.4 4.6 C312 4.7 1.8 C2 3.8 Use Lpd, Sp, Rpd with p=ClassAvg and d=unitized ClassSum. All 6 class hulls separated using Lpd, p=CLavg, D=CLsum. D311 separates C311, D312 separates C312 and D313 separates C313 from all others. D2 separates C11 and C2. Now, remove some false positives with S and R using the same p's and d's: D11=C11 p=avC11 Sp [1.6]C11 [3.4 4 4]C311 [5.4 6]C313 [2.4 4.4]C2 [5]C312 D2=C2 p=avC2 Sp [2 2.3]C11 [4.5 5.8]C313 [1.8 3.5]C2 [5 5.1]C312 [2.5 3.5]C311 D313=C313 p=avC313 Sp [3.5 4.2]C11 [6.5]C312 [2.8 6.2]C2 [3.8 6.2]C311 [2.5 3.5]C313 D311=C311 p=avC311 Sp [1.2]C311 [4.2]C11 [6.2 7.2]C312 [2.2 6.2]C2 [6.2 8.2]C313 D312=C312 p=avC312 Sp [3.5 4.5]C11 [6.5 7.5]C313 [4.5 6.5]C2 [2.5]C312 [5.5]C311 Sp removes a lot of the potential for false positives. (Many of the classes lie a single distance from p.) D11=C11 p=avC11 Rpd [1.2]C11 [1.4 2]C2 [1.7 2]C311 [2.2 2.]]C312 [2.2 2.4]C313 D1=TS p=avTS Rpd [1.3 1.4]C11 [1.3 1.9]C2 [1.5 1.8]C311 [2.1]C312 [2.0 2.2]C313 D2=C2 p=avC2 Rpd [1.3 1.4]C11 [1.3 1.8]C2 [1.6 1.8]C311 [2.2]C312 [2.1 2.4]C313 D313=C313 p=avC313 Rpd [1.3 1.4]C11 [1.3 2]C2 [1.6 2]C311 [2.2]C312 [1.5 1.8]C313 D312=C312 p=avC312 Rpd [1.3 1.4]C11 [1.4 2]C2 [1.7 1.9]C311 [1.5]C312 [2.2 2.4]C313 D311=C311 p=avC311 Rpd [1.4]C11 [1.2 2]C2 [1.1]C311 [2.2]C312 [2.2 2.4]C313 Rpd removes even more of the potential for false positives.

  15. Test Set FAUST Hull Classification 2 (TESTING) D1=TS p=avTS Rpd [1.3 1.4]C11 [1.3 1.9]C2 [1.5 1.8]C311 [2.1]C312 [2.0 2.2]C313 D1=TS p=avTS Sp C11={3} [1.9 2.1]C11 [2.4 3.4]C311 [4.2 5.4]C313 D1=TS p=avTS Lpd [4.6 4.7]C312 [1.8 3.8]C2 C2 ={4,14,23, 45} [.57]C311 [.31 .44]C312 [-.09 .11]C11 [.11 .44]C2 [.51 .77]C313 C311= {29} C312={13} C313={26} D11=C11 p=avC11 Rpd [1.2]C11 [1.4 2]C2 [1.7 2]C311 [2.2 2.]]C312 [2.2 2.4]C313 D11=C11 p=avC11 Sp [1.6]C11 [3.4 4 4]C311 [5.4 6]C313 [2.4 4.4]C2 [5]C312 D11=C11 p=avC11 Lpd [0]C311 [.31]C312 O={18 49 6 39 21 46 7 35} [.63]C11 [0 .31]C313 [0 .63]C2 D2=C2 p=avC2 Lpd D2=C2 p=avC2 Sp [2 2.3]C11 [4.5 5.8]C313 [1.8 3.5]C2 [5 5.1]C312 [2.5 3.5]C311 D2=C2 p=avC2 Rpd [1.3 1.4]C11 [2.1 2.4]C313 [1.3 1.8]C2 [1.6 1.8]C311 [2.2]C312 .[44 .66]C313 [0 .22]C11 [.44 .77]C2 [.66] C311 [.11 .22]C312 D311=C311 p=avC311 Sp [1.2]C311 [4.2]C11 [6.2 7.2]C312 [2.2 6.2]C2 [6.2 8.2]C313 D311=C311 p=avC311 Rpd [1.4]C11 [1.2 2]C2 [1.1]C311 [2.2]C312 [2.2 2.4]C313 D311=C311 p=avC311 Lpd [0]C11 [1.3 1.6]C311 [0 .33]C312 [0 .33]C313 [0 .66]C2 D312=C312 p=avC312 Lpd D312=C312 p=avC312 Sp [3.5 4.5]C11 [6.5 7.5]C313 [4.5 6.5]C2 [2.5]C312 [5.5]C311 D312=C312 p=avC312 Rpd [1.3 1.4]C11 [2.2 2.4]C313 [1.4 2]C2 [1.7 1.9]C311 [1.5]C312 .31 C11 .31 C2 .31 C311 .31 C313 1.58 C312 D313=C313 p=avC313 Rpd [1.3 1.4]C11 [1.3 2]C2 [1.6 2]C311 [2.2]C312 [1.5 1.8]C313 D313=C313 p=avC313 Sp [3.5 4.2]C11 [6.5]C312 [2.8 6.2]C2 [3.8 6.2]C311 [2.5 3.5]C313 D313=C313 p=avC313 Lpd [0 .22]C11 [0 .44]C2 [0 .44]C311 [1.3 1.6]C313 [.22]C312 ε=.8 predicted Class 11 2 2 2 311(all 311|2 all) 312(all 312|313 a Other . . . . . . . Other D=TS Rpd Sp Lpd trueCL Predicted____CLASS Final R S L predicted 1.41 2.19 -0.4 11 d3 2 Oth 11 Other 1.40 2.06 -0.3 2 d4 2 2 11 Other 1.92 3.71 0.01 2 d14 Oth 2 11 Other 1.38 1.99 -0.2 2 d23 2|11 2|11 Oth Other 1.97 3.92 -0.1 311 d29 Oth 312|313 11 Other 2.22 4.99 -0.2 312 d13 313 313 11 Other 2.60 6.78 -0.0 313 d26 Oth Oth 11 Other 1.40 2.13 -0.3 d6 2|11 2 11 Other 2.50 6.37 0.34 d7 313 Oth 2 Other 1.40 2.06 -0.3 d18 2|11 2|11 Oth Other 2.42 5.92 -0.1 d21 313 Oth Oth Other 3.46 12.2 0.47 d35 Oth Oth Oth Other 2.60 6.78 -0.0 d39 Oth Oth 11 Other 2.35 5.57 0.14 d46 Oth Oth 2 Other 1.41 2.19 -0.4 d49 2 2 Oth Other 8/15 = 53% correct just with D=TS p=AvgTS Note: It's likely to get worse as we consider more D's. Let's think about TrainingSet quality resulting from clustering. This a poor quality TrainingSet (from clustering Mother Goose Rythmes. MGR is a difficult corpus to cluster since: 1., in MGR, almost every document is isolated (an outlier), so the clustering is vague (no 2 MGRs deal with the same topic so their word use is quite different.). Instead of tightening the class hulls by replacing CLASSmin and CLASSmax by CLASSfpci (fpci=first percipitous count increase) and CLASSlpcd, we might loosen class hulls (since we know the classes somewhat arbitrary) by expanding the [CLASSmin, CLASSmax] interval as follows: Let A = Avg{ClASSmin, CLASSmax} and R (for radius) = A-CLASSmin (=CLASSmax-A also). Use [A-R-ε, A+R+ε]. Let ε=.8 increases accuracy to 100% (assuming all Other stay Other.). Finally, it occurs to me that Clustering to produce a TrainingSet, then setting aside a TestSet gives a good way to measure the quality of the clustering. If the TestSet part classifies well under the TrainingSet part, the clustering must have been high quality (produced a good TrainingSet for classification). This clustering quality test method is probably not new (check the literature?). If it is new, we might have a paper here? (discuss this quality measure and assess using different ε's?)

  16. WP Wed 11/26 Yes, we have discovered also that one has to think about the quality of the training set.   If it is very high quality (expected to fully encompass all borderline cases of all classes) then using exact gap endpoints is probably wise, but if there is reason to worry about the comprehensiveness of the training set (e.g., when there are very few training samples - which is often the case in medical expert systems where getting a sufficient number of training samples is difficult and expensive), then it is probably better to move the cutpoints toward the midpoint (reflecting the vagueness of training set class boundaries).  What does one use to decide how much to move away from the endpoints?  That's not an easy question.  Cluster deviation seems like a useful measure to employ. One last though on how to decide whether to cut at gap midpoint, endpoints, or to move the cut-points away from the endpoints toward the midpoint, If one has a time-stamp on training samples, one might assess the "class endpoint" change rate over time. As the training set gets larger and larger, if an endpoint stops moving much and isn't an outlier, then cutting at the endpoint seems wise.   If an endpt is still changing a lot, then moving away from that endpoint seems wise (maybe based on the rate of change of that endpoint as well as other measures?). A complete subgraph is a clique. A maximal clique is not a proper subset of any other clique. In G=(X,Y,E), a bipartite graph, a clique (Sx, Sy) is a complete bipartite subgraph induced by bipartite vertex set (Sx, Sy). The Consensus Set or clique of Sx, CLQ(Sx) = xSxNy(x), i.e., the set of all y's that are adjacent (edge connected) to every x in Sx. Clearly, (Sx, CLQ(Sx)) is a clique. Thm2:  SyY s.t. CLQ(Sy) ( CLQ(Sy), CLQ(CLQ(Sy)) ) is maximal. Thm1: (Sx, Sy) is a maximal clique iff Sy=CLQ(Sx) and Sx=CLQ(Sy) Find all cliques starting with Sy=singletons. Then examine Sy1y2-doubletons s.t. Px(Sy1y2) Then tripletons etc. Examining MGRs, (x=docs, y=words) all singleton wordsets, Sy, form a nonempty clique. AND pairwise to find all nonempty doubleton wordset cliques, Sy1y2. AND those nonempty doubleton wordset with each other singleton wordset to find all nonempty tripleton wordset cliques, Sy1y2y3... Start w singleton docs, incl another... until . The last nonempty set is a max-clique and all subsets are cliques. Remove them. Iterate. 7 13 w4 7 35 42 w7 7 35 w10 7 13 33 45 w13 7 30 43 w24 7 9 23 29 45 w42 7 10 11 12 25 41 w44 7 13 w4 w13 7 35 w7 w10 7 45 w13 w42 10 11 12 25 41 w44 10 14 w42 10 10 44 w32 10 21 w12 w19 2 37 w57 2 46 w45 2 47 w57 2 37 47 w57 #CLQs #docs #words 13 2 2 1 6 1 7 5 1 9 4 1 23 3 1 48 2 1 4 8 w25 4 29 w2 4 30 w2 4 35 w25 4 39 w2 4 46 w2 w25 4 50 w25 4 8 35 46 50 w25 4 29 30 39 46 w2 1 8 w58 1 14 w21 1 17 w49 1 23 w52 1 28 w52 1 30 w49 1 41 w52 1 46 w49 1 48 w52 1 8 none 1 14 none 1 17 30 46 w49 1 23 28 41 48 w52 1 28 23 41 48 1 30 17 46 1 41 23 28 48 1 46 17 30 1 48 23 28 41 11 12 25 41 w44 11 14 17 32 36 w38 11 35 37 w17 3 13 w51 3 29 w8 3 46 w51 3 47 w8 3 13 46 w51 3 29 47 w8 12 25 41 w44 12 25 w59 12 26 w15 12 25 w44 w59 5 10 14 w26 5 11 17 32 36 w38 5 36 41 w34 5 36 w34 w38 8 26 w16 8 35 46 50 w25 9 26 27 45 w3 9 27 29 45 w42 9 44 w35 9 45 w3 w42 6 15 18 32 w22 6 49 w5 • There is something wrong here. • This does not find all maximal cliques. • Next I try the following logic: • Find all 1WdC (1 Word Cliques). • A kWdC contains each of k (k-1)WdCs, so of a (k-1) wordset is not the wordset of a clique than none of its supersets are either (downward closure property). • Thus, the wordset of any 2WdCs can be composed by unioning the wordsets of two 1WdCs and any k WdCwordset is the union o f a (k-1)WdCwordset with a 1WdCwordset. 18 32 w22 18 44 w10 13 21 50 w47 13 23 w4 13 33 45 w13 13 21 43 w54 13 46 w51 13 21 w47 w54 14 17 32 36 w47 14 39 w55 16 33 37 w48 16 28 w6 23 28 41 48 w52 27 35 w43 27 50 w53 21 35 w10 21 42 w4 21 43 w54 21 50 w47 25 41 w44 17 30 46 w42 17 32 36 w38 17 39 47 w18 17 48 w56 28 35 w28 28 38 46 w20 28 39 50 w9 28 41 48 w52 15 18 32 w22 15 35 w50 15 44 w31 26 27 45 w3 26 28 w60 26 29 w1 26 38 w36 26 39 w30 22 35 w43 22 50 w53 29 45 w28 29 47 w8 29 30 39 46 w2 33 37 w48 33 45 w13 33 49 w29 37 47 w41 w57 45 38 46 w57 46 39 41 w39 39 46 w2 39 47 w18 39 50 w9 w45 47 50 w25 30 32 41 w27 30 35 w23 30 43 w24 30 46 w2 w49 35 36 w33 35 37 w17 35 38 w40 35 39 w45 35 41 w34 35 42 w7 48 49 41 48 w52 32 36 w22 32 41 w27 50 44 42 43 36

  17. 1 1 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 d1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 d2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 d3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 d4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 d5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 d6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 d7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 d8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 d9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 d10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 d11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 d12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 d13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 d14 0 0 0 0 0 0 0 0 0 0 0 0 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0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 2 3 4 5 6 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 & w1 1 1 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 00 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 & w2 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 2 0 1 0 0 1 0 0 00 0 0 0 0 1 0 0 1 0 0 1 1 0 0 2 0 1 0 0 0 1 0 0 0 0 0 & w3 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 • A first goal of our team might be to implement (in optimized Treeminer parallel code ), that is, a maximal complete subgraph finder (maximal clique finder). The benefits would be substantial! • This would be an exercise in parallel programming (e.g., in a TreeminerHadoop environment). • This is a typical exponential growth case. If you can find an engineering breakthru here, it will be a breakthru for a massive collection of similar existing big data parallel programming problems . & w4 0 0 1 0 0 1 0 0 2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 1 1 0 1 0 0 0 0 0 0 & w5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 & w7 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 & w8 0 0 2 0 0 1 1 0 0 1 0 0 0 0 0 1 1 1 0 0 1 0 0 0 0 1 1 0 0 1 0 0 1 0 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 & w9 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 & w10 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 & w11 0 1 1 1 0 0 1 0 1 0 0 0 1 1 1 0 0 1 0 0 0 0 1 1 0 0 1 0 0 1 0 1 1 1 1 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 & w12 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 & w13 0 1 0 0 0 0 2 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 & w14 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 2 0 1 0 0 1 1 0 0 1 0 0 1 0 0 0 0 0 0 What is the next step? We would have to have all the wh&wk, k>h results (not just the 1 counts) but that would have taken about 60 more slides to display ;-( Just looking at the pTee results of w1&wk k>1 above, we see that even though w1&w2 and w1&w3 both have counts=1, their AND (which is w1&w2&w3) has 0 count and therefore we need not consider any combinations of the type w1&w2&w3&… by the downward closure). In fact, the only wh for which we need to look further is h=42. Note that ct(w1&w2&w42)=1 but all other ct(w1&w2&wh)=0 The only maximal clique involving w1 and w2 is {DocSet={29}, WordsSet={1,2,42}, right? Next we would look at the pTrees of w2&wk, k>2. Clearly we only need to consider the 16 WDpTrees {8,9,18,20,23,24,25,27,30,39,42,45,46,49,51,55}, not all 58 of them. And going down to w30 the WDpTreeSet is (30,48} only To appreciate that we need engineering breakthroughs here, recall that a typical vocabulary might be 100,000 words, not just 60. & w15 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 & w16 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 & w17 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 & w18 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 1 1 0 0 1 1 0 1 1 0 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 & w19 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 & w20 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 & w21 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 1 1 0 0 0 0 0 0 0 1 & w22 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 & w23 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 & w24 1 1 0 1 1 0 0 0 0 1 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 & w25 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 & w26 0 0 1 0 0 0 0 1 1 0 0 1 0 0 1 0 0 1 0 2 1 1 0 1 1 1 0 1 0 0 0 0 1 0 0 & w27 0 0 0 0 0 1 0 1 0 0 0 2 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 & w28 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 & w29 0 0 0 0 1 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 & w30 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 & w31 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 & w32 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 & w33 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 & w34 1 0 0 1 1 0 1 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 & w35 0 0 1 1 1 1 0 0 1 1 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 & w36 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 & w37 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 & w38 0 0 1 0 0 1 0 1 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 & w39 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 & w40 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 & w41 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 & w42 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 2 0 0 0 & w43 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 & w43 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 & w44 0 0 0 0 0 0 0 1 0 0 0 0 0 0 2 0 & w45 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 & w46 0 0 1 0 1 0 0 0 0 0 1 0 0 0 & w47 0 0 0 1 0 1 2 0 0 0 0 0 0 & w48 0 0 0 0 0 0 0 0 1 0 0 0 & w49 0 1 1 0 0 0 1 0 1 0 0 & w50 0 0 0 0 0 0 0 0 0 0 & w51 0 0 1 0 0 0 0 0 0 & w52 0 0 0 1 0 1 0 1 & w53 0 0 0 0 0 0 0 & w54 0 0 0 0 0 0 & w55 0 0 0 0 0 & w56 0 0 0 0 & w57 0 0 0 & w58 0 0 & w59 0

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