By Minos Garofalakis, Rajeev Rastogi (auth.), Ming-Syan Chen, Philip S. Yu, Bing Liu (eds.)
Knowledge discovery and knowledge mining became components of turning out to be value end result of the contemporary expanding call for for KDD thoughts, together with these utilized in computing device studying, databases, facts, wisdom acquisition, facts visualization, and excessive functionality computing. In view of this, and following the good fortune of the 5 past 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 examine effects, cutting edge rules, state of the art advancements, and implementation studies in wisdom discovery and knowledge mining between researchers in educational and commercial corporations. a lot paintings went into getting ready a software of top of the range. We got 128 submissions. each paper used to be reviewed by way of three application committee individuals, and 32 have been chosen as typical papers and 20 have been chosen as brief papers, representing a 25% reputation fee for normal papers. The PAKDD 2002 software was once extra better through keynote speeches, introduced by way of Vipin Kumar from the Univ. of Minnesota and Rajeev Rastogi from AT&T. additionally, PAKDD 2002 used to be complemented by means of 3 tutorials, XML and knowledge mining (by Kyuseok Shim and Surajit Chadhuri), mining consumer information throughout a number of shopper touchpoints at- trade websites (by Jaideep Srivastava), and knowledge clustering research, from basic 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
Za¨ıane et al. References 1. , Gunopulos D. and Raghavan P. (1998) Automatic subspace clustering of high dimensional data for data mining applications. In Proc. ACMSIGMOD Int. Conf. Management of Data, pp 94–105. 2. , Sander J. and Xu X. (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In Proc. ACM-SIGKDD Int. Conf. Knowledge Discovery and Data Mining, pp 226–231. 3. Estivill-Castro V. and Lee I. (2000) Autoclust+: Automatic clustering of pointdata sets in the presence of obstacles.
Data, handle mismatches in format, structure, as well as semantics, and normalization and integration. A very good book on the subject is [Pyle99]. Once the data has been cleaned up, various data mining algorithms can be applied to extract models from it. A number of data mining techniques have been developed, and the one to be applied depends on the specific purpose at hand. [HMS00] provides and excellent introduction to various data mining algorithms, while [Rud00] shows how they can be applied in the context of marketing.
Kestler H. and Palm G. (2000) An algorithm for adaptive clustering and visualisation of highdimensional data sets. -J. L. G. della Riccia, R. Kruse, editor, Computational Intelligence in Data Mining, pp 127–140. Springer, Wien, New York. 23. Sharma S. (1996) Applied Multivariate Techniques. John Willey & Sons. 24. , Chatterjee S. and Zhang A. (1998) Wavecluster: a multiresolution clustering approach for very large spatial databases. In Proc. 24th Conf. on Very Large Data Bases. 25. Smyth P. (1996) Clustering using monte carlo cross-validation.
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.)