By Minos Garofalakis, Rajeev Rastogi (auth.), Ming-Syan Chen, Philip S. Yu, Bing Liu (eds.)
Knowledge discovery and knowledge mining became components of transforming into importance as a result of the contemporary expanding call for for KDD recommendations, together with these utilized in computing device studying, databases, facts, wisdom acquisition, info visualization, and excessive functionality computing. In view of this, and following the good fortune of the 5 earlier PAKDD meetings, the 6th Pacific-Asia convention on wisdom Discovery and information Mining (PAKDD 2002) aimed to supply a discussion board for the sharing of unique learn effects, cutting edge rules, state of the art advancements, and implementation stories in wisdom discovery and information mining between researchers in educational and business companies. a lot paintings went into getting ready a application of top of the range. We bought 128 submissions. each paper used to be reviewed through three application committee contributors, and 32 have been chosen as standard papers and 20 have been chosen as brief papers, representing a 25% attractiveness fee for normal papers. The PAKDD 2002 software was once extra greater via keynote speeches, brought by way of Vipin Kumar from the Univ. of Minnesota and Rajeev Rastogi from AT&T. additionally, PAKDD 2002 was once complemented via 3 tutorials, XML and information mining (by Kyuseok Shim and Surajit Chadhuri), mining purchaser info throughout quite a few purchaser touchpoints at- trade websites (by Jaideep Srivastava), and knowledge clustering research, from uncomplicated groupings to scalable clustering with constraints (by Osmar Zaiane and Andrew Foss).
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Extra resources for Advances in Knowledge Discovery and Data Mining: 6th Pacific-Asia Conference, PAKDD 2002 Taipei, Taiwan, May 6–8, 2002 Proceedings
ACMSIGKDD Int. Conf. Knowledge Discovery and Data Mining. 26. , Karypis G. and Kumar V. (2000) A comparison of document clustering techniques. In SIGKDD Workshop on Text Mining. 27. Theodoridis S. and Koutroubas K. (1999) Pattern recognition, Academic Press. 28. Tung A. K. , Hou J. and Han J. (2001) Spatial clustering in the presence of obstacles. In Proc. ICDE Int. Conf. On Data Engineering. 29. Tung A. K. , Lakshmanan L. V. S. and Han J. (2001) Constraint-based clustering in large databases. In Proc.
Html [Pyle99] Pyle, Dorian, “Data Preparation for Data Mining”, Morgan Kaufmann Publishers, 1999, ISBN No. , “Data Mining, Cookbook: Modeling Data for Marketing, Risk and Customer Relationship Management”, John Wiley and Sons, 2000. html [Swif] Ronald S. html. html [Michigan] University of Michigan Business School Study, “American Customer Satisfaction Index”, 2000. On Data Clustering Analysis: Scalability, Constraints, and Validation Osmar R. Za¨ıane, Andrew Foss, Chi-Hoon Lee, and Weinan Wang University of Alberta, Edmonton, Alberta, Canada Abstract.
A rule R : C1 ⇒ C2 has a conﬁdence c, if the c% of the records of the database that contain C1 also contain C2 . The goal of the techniques that search for association rules is to extract only those that exceed some minimum values of support and confidence that are deﬁned by the user. The greater part of the algorithms that extract association rules work in two phases: in the ﬁrst one they try to ﬁnd the sets of attributes that exceed the minimum value of support and, in the second phase, departing from the sets discovered formerly, they extract the association rules that exceed the minimum value of conﬁdence.
Advances in Knowledge Discovery and Data Mining: 6th Pacific-Asia Conference, PAKDD 2002 Taipei, Taiwan, May 6–8, 2002 Proceedings by Minos Garofalakis, Rajeev Rastogi (auth.), Ming-Syan Chen, Philip S. Yu, Bing Liu (eds.)