Advances in Knowledge Discovery and Data Mining, Part II: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010, Proceedings
Mohammed J. Zaki, Jeffrey Xu Yu, B. Ravindran, Vikram Pudi
Springer Science & Business Media, Jun 1, 2010 - Computers - 520 pages
The14thPaci?c-AsiaConferenceonKnowledgeDiscoveryandData Mining was held in Hyderabad, India during June 21-24, 2010; this was the ?rst time the conference was held in India. PAKDDisamajorinternationalconferenceintheareasofdatamining (DM) and knowledge discovery in databases (KDD). It provides an international - rum for researchers and industry practitioners to share their new ideas, original research results and practical development experiences from all KDD-related areas including data mining, data warehousing, machine learning, databases, statistics, knowledge acquisition and automatic scienti?c discovery, data visu- ization, causal induction and knowledge-based systems. PAKDD-2010 received 412 research papers from over 34 countries incl- ing: Australia, Austria, Belgium, Canada, China, Cuba, Egypt, Finland, France, Germany, Greece, Hong Kong, India, Iran, Italy, Japan, S. Korea, Malaysia, Mexico, TheNetherlands, NewCaledonia, NewZealand, SanMarino, Singapore, Slovenia, Spain, Switzerland, Taiwan, Thailand, Tunisia, Turkey, UK, USA, and Vietnam. This clearly re?ects the truly international stature of the PAKDD conference. AfteraninitialscreeningofthepapersbytheProgramCommitteeChairs, for papers that did not conform to the submission guidelines or that were deemed not worthy of further reviews, 60 papers were rejected with a brief expla- tion for the decision. The remaining 352 papers were rigorously reviewed by at least three reviewers. The initial results were discussed among the reviewers and ?nally judged by the Program Committee Chairs. In some cases of c- ?ict additional reviews were sought. As a result of the deliberation process, only 42 papers (10.2%) were accepted as long presentations (25 mins), and an ad- tional 55 papers (13.3%) were accepted as short presentations (15 mins). The total acceptance rate was thus about 23.5% across both categories.
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AdaBoost algorithm analysis applied approach average BASSET-N Berlin Heidelberg 2010 blog class label classiﬁers clustering CluStream component computation core data mining data streams databases datasets DBLP decision tree decompositions deﬁned Deﬁnition denote diﬀerent dimension dimensional distance distribution document edges eﬀective eﬃcient eﬀort ensemble Euclidean distance evaluation EverQuest II example experiments extracted feature ﬁnd ﬁrst function genes given global graph mining graph sequence identiﬁed inﬂuence input instances Isomap ISS sets itemset kernel keyword linear LNAI M.J. Zaki machine learning matrix measure method mixture model must-link constraints nearest neighbors Nonnegative Matrix opinion optimal outliers PAKDD paper parameters pattern mining peer-to-peer peers perceptron performance prediction problem query nodes random relevance retrieval rules samples score Section selection semi-supervised sequential patterns signiﬁcant signiﬁcantly similar space speciﬁc structure subgraphs suﬃx support vector machines techniques timestamp values WDKNN WDTW weight