Advances in Knowledge Discovery and Data Mining, Part I: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabat, India, June 21-24, 2010, Proceedings
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 cat
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a-group accuracy analysis approach association rules attributes AUC values Berlin Heidelberg 2010 classiﬁer clustering algorithms computed concept drift conﬁdence correlation corresponding data mining database dataset deep web deﬁned Deﬁnition denoted diﬀerent distance distribution document eﬀective eﬃcient emerging patterns episode evaluate experiments feature ﬁlter ﬁnd ﬁnding ﬁrst frequent itemsets function given graph Heidelberg inﬂuence itemset-sequences itemsets iterations K-means K-Medoids Keywords L2 norm Latent Dirichlet Allocation learning LNAI loss functions M.J. Zaki Machine Learning matrix maximize measure method metric minimal nodes number of clusters number of topics objects optimization PageRank PAKDD paper parameters pattern mining performance problem proposed protocol pruning query random ranking ratings recoding represents RSS protocol runtime sample Section sequence sequential patterns signiﬁcant similar skyline social network spatial speciﬁc Springer-Verlag Berlin Heidelberg Table techniques trajectory transaction tuple vector weights