By Philip S. Yu (auth.), Honghua Dai, Ramakrishnan Srikant, Chengqi Zhang (eds.)
ThePaci?c-AsiaConferenceonKnowledgeDiscoveryandDataMining(PAKDD) has been held each year considering that 1997. This 12 months, the 8th within the sequence (PAKDD 2004) was once held at Carlton Crest lodge, Sydney, Australia, 26–28 might 2004. PAKDD is a number one foreign convention within the region of information mining. It p- vides a global discussion board for researchers and practitioners to proportion their new rules, unique examine effects and functional improvement stories from all KDD-related parts together with facts mining, facts warehousing, computer studying, databases, statistics, wisdom acquisition and automated scienti?c discovery, info visualization, causal induction, and knowledge-based structures. the choice approach this yr was once super aggressive. We acquired 238 researchpapersfrom23countries,whichisthehighestinthehistoryofPAKDD, and re?ects the popularity of and curiosity during this convention. each one submitted learn paper used to be reviewed by means of 3 contributors of this system committee. F- lowing this autonomous evaluation, there have been discussions one of the reviewers, and whilst worthwhile, extra stories 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 fee of roughly 34%. The convention accommodated either learn papers offering unique - vestigation effects and commercial papers reporting genuine information mining functions andsystemdevelopmentexperience.Theconferencealsoincludedthreetutorials on key applied sciences of information discovery and knowledge mining, and one workshop targeting speci?c new demanding situations and rising problems with wisdom discovery anddatamining.ThePAKDD2004programwasfurtherenhancedwithkeynote speeches by means of impressive researchers within the zone of information discovery and knowledge mining: Philip Yu, supervisor of software program instruments and methods, IBM T.J.
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Additional info for Advances in Knowledge Discovery and Data Mining: 8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 26-28, 2004. Proceedings
Construct a one-vs-others SVM ensemble. For each class i, leave out the entire class j from the ‘others’ set if Mij > β. If the parameter β is very small a lot of classes will be excluded from the others set. If it is too small, none of the classes may be excluded resulting in the original ensemble. β is chosen by cross-validation. ConfMat is faster to train than BandSVM, relying on a confusion matrix given by a fast NB classiﬁer, and requires only one SVM ensemble to be trained. g. hierarchies of classes) can be easily incorporated in ConfMat.
J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann. 2000. 11. J. Han, M. Kamber, and A. Tung. Spatial Clustering Methods in Data Mining: A Survey. , Geographic Data Mining and Knowledge Discovery. Taylor and Francis. 21, 2001. 12. -R. -S. Chen. A robust and eﬃcient clustering algorithm based on cohesion self-merging. In Proc. Of the 8th ACM SIGKDD Internal Conf. On Knowledge Discovery and Data Mining, August 2002. 13. E. Schikuta. Grid-Clustering: A Fast Hierarchical Clustering Method for Very Large Data Sets.
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.
Advances in Knowledge Discovery and Data Mining: 8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 26-28, 2004. Proceedings by Philip S. Yu (auth.), Honghua Dai, Ramakrishnan Srikant, Chengqi Zhang (eds.)