By Haizheng Zhang, Myra Spiliopoulou, Bamshad Mobasher, C. Lee Giles, Andrew McCallum, Olfa Nasraoui, Jaideep Srivastava, John Yen
This publication constitutes the completely refereed post-workshop court cases of the ninth foreign Workshop on Mining net info, WEBKDD 2007, and the first overseas Workshop on Social community research, SNA-KDD 2007, together held in St. Jose, CA, united states in August 2007 along side the thirteenth ACM SIGKDD foreign convention on wisdom Discovery and information Mining, KDD 2007.
The eight revised complete papers provided including a close preface went via rounds of reviewing and development and have been conscientiously chosen from 23 preliminary submisssions. the improved papers tackle all present matters in internet mining and social community research, together with conventional internet and semantic internet purposes, the rising functions of the internet as a social medium, in addition to social community modeling and analysis.
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Additional resources for Advances in Web Mining and Web Usage Analysis: 9th International Workshop on Knowledge Discovery on the Web, WebKDD 2007, and 1st International Workshop
By performing behavior analysis and determining the communication patterns we are able to automatically: – Rank the major oﬃcers of an organization. – Group similarly ranked and connected users in order to accurately reproduce the organizational structure in question. – Understand relationship strengths between speciﬁc segments of users. This work is a natural extension of previous work on the Email Mining Toolkit project (EMT) [32,33]. New functionality has been introduced into the EMT system for the purposes of automatically extracting social hierarchy information from any email collection.
If you want to experience that you might want to go to disneyland to see/feel ‘honey I shrunk the audience”’ 3. “The possible future development in entertainment will be the digital eye glasses with embedded intelligence in form of digital eye-glasses. ” P1 P2 Descriptive Stemmed Words 49 35 patient, doctor, healthcar, diagnosi, hospit, medic, prescript, medicin, treatment, drug, pharmaci, nurs, physician, clinic, blood, prescrib, phr, diagnost, diseas, health Digital Me 26 23 scrapbook, music, dvd, song, karaok, checker, entertain, movi, album, content, artist, photo, video, media, tivo, piraci, theater, audio, cinema Simpliﬁed Business Engines 26 23 smb, isv, back-oﬃc, eclips, sap, mashup, business-in-a-box, invoic, erp, mgt, oracl, app, salesforc, saa, host, procur, payrol, mash, crm Integrated Mass Transit Informa- 59 20 bus, congest, passeng, traﬃc, railwai, commut, rout, lane, destin, transit, tion System journei, rail, road, vehicl, rider, highwai, gp, driver, transport Big Green innovations 27 13 desalin, water, rainwat, river, lawn, irrig, rain, ﬁltrat, puriﬁ, potabl, osmosi, contamin, purif, drink, nanotub, salt, pipe, rainfal, agricultur 3-D Internet 22 12 password, biometr, debit, authent, ﬁngerprint, wallet, ﬁnger, pin, card, transact, atm, merchant, reader, cellular, googlepag, wysiwsm, byte, userid, encrypt Intelligent Utility Network 23 9 iun, applianc, peak, thermostat, quickbook, grid, outag, iug, shut, holist, hvac, meter, heater, household, heat, resours, kwh, watt, electr, fridg Branchless Banking 11 9 branchless, banker, ipo, bank, cr, branch, deposit, clinet, cv, atm, loan, lender, moeni, withdraw, teller, mobileatm, transact, wei, currenc, grameen Real-Time Translation Services 33 5 mastor, speech-to-speech, speech, languag, english, nativ, babelﬁsh, translat, troop, multi-lingu, doctor-pati, cn, lanaguag, inno, speak, arab, chines, barrier, multilingu Finalist Ideas for Funding Electronic Health Record System Table 2.
The results from diﬀerent clustering algorithms are similar and therefore we only discuss the ones using the completelinkage agglomerate clustering algorithm. For implementation,we use the open source software CLUTO1 . 3 Clustering Results As discussed above, we use the document clustering algorithms to analyze the threads in Phase 1 and Phase 2. In the experiment, we preset the number of clusters to 100. Several interesting observations can be made by examining the clustering results: (1) Phase 1 to Phase 2: since we are interested in ﬁnding out the overlapping topics between the threads in Phase 1 and those in Phase 2, we plot the histogram on the number of threads from Phase 2 in each cluster in Figure 8.
Advances in Web Mining and Web Usage Analysis: 9th International Workshop on Knowledge Discovery on the Web, WebKDD 2007, and 1st International Workshop by Haizheng Zhang, Myra Spiliopoulou, Bamshad Mobasher, C. Lee Giles, Andrew McCallum, Olfa Nasraoui, Jaideep Srivastava, John Yen