By Philip S. Yu (auth.), Honghua Dai, Ramakrishnan Srikant, Chengqi Zhang (eds.)

ThePaci?c-AsiaConferenceonKnowledgeDiscoveryandDataMining(PAKDD) has been held each year given that 1997. This 12 months, the 8th within the sequence (PAKDD 2004) used to be held at Carlton Crest inn, Sydney, Australia, 26–28 could 2004. PAKDD is a number one foreign convention within the zone of information mining. It p- vides a world discussion board for researchers and practitioners to proportion their new principles, unique examine effects and sensible improvement reviews from all KDD-related components together with info mining, facts warehousing, computing device studying, databases, data, wisdom acquisition and automated scienti?c discovery, facts visualization, causal induction, and knowledge-based structures. the choice strategy this 12 months used to be tremendous aggressive. We bought 238 researchpapersfrom23countries,whichisthehighestinthehistoryofPAKDD, and re?ects the popularity of and curiosity during this convention. each one submitted examine paper was once reviewed via 3 individuals of this system committee. F- lowing this self sustaining assessment, there have been discussions one of the reviewers, and whilst worthy, extra reports from different specialists have been asked. a complete of fifty papers have been chosen as complete papers (21%), and one other 31 have been chosen as brief papers (13%), yielding a mixed popularity price of roughly 34%. The convention accommodated either study papers providing unique - vestigation effects and commercial papers reporting actual info mining functions andsystemdevelopmentexperience.Theconferencealsoincludedthreetutorials on key applied sciences of data discovery and knowledge mining, and one workshop concentrating on speci?c new demanding situations and rising problems with wisdom discovery anddatamining.ThePAKDD2004programwasfurtherenhancedwithkeynote speeches through remarkable researchers within the sector of information discovery and information mining: Philip Yu, supervisor of software program instruments and strategies, IBM T.J.

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Additional resources for Advances in Knowledge Discovery and Data Mining: 8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 26-28, 2004. Proceedings

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Often it is not sufficient to talk about a document belonging to a single class. Based on the granularity and coverage of the set of classes, a document is often about more than one topic. A document describing the politics involved in the sport of cricket, could be classified as Sports/Cricket, as well as Society/Politics. When a document can belong to more than one class, it is called multi-labeled. Multi-labeled classification is a harder problem than just choosing one out of many classes. H. Dai, R.

Crammer and Y. Singer. A family of additive online algorithms for category ranking. Journal of Machine Learning Research, 1025–1058, 2003. 7. A. Elisseeff and J. Weston. Kernel methods for multi-labelled classification and categorical regression problems. Technical Report, BioWulf Technologies, 2001. 8. H. Yu, J. Han, and K. C-C. Pebl: Positive example-based learning for web page classification using SVM. In Proceedings of ACM SIGKDD-2002. 9. A. McCallum. Multi-label text classification with a mixture model trained by EM.

Fraleigh. A First Course in Abstract Algebra, 6th edition. Addison Wesley. 1999. 8. A. Hinneburg and D. A. Keim. An efficient approach to clustering in large multimedia databases with noise. Proc. 1998 Int. Conf. Knowledge Discovery and Data Mining (KDD-98), pages 58-65, New York, August 1998. 9. A. Hinneburg and D. A. Keim. Optimal Grid-Clustering: Towards Breaking the curse of Dimensionality in High-Dimensional Clustering. VLDB, 1999. 10. J. Han and M. Kamber. Data Mining: Concepts and Techniques.

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