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Download Data Warehousing and Knowledge Discovery: 4th International by Marco Botta, Jean-Francois Boulicaut, Cyrille Masson, Rosa PDF

By Marco Botta, Jean-Francois Boulicaut, Cyrille Masson, Rosa Meo (auth.), Yahiko Kambayashi, Werner Winiwarter, Masatoshi Arikawa (eds.)

Within the previous few years info Warehousing and information Discovery know-how has validated itself as a key expertise for corporations that desire to enhance the standard of the consequences got from information research, choice aid, and the automated extraction of information from info. The Fourth foreign convention on facts Warehousing and data Discovery (DaWaK 2002) keeps a sequence of winning meetings devoted to this subject. Its major target is to compile researchers and practitioners to debate learn matters and event in constructing and deploying information warehousing and data discovery platforms, functions, and strategies. The convention makes a speciality of the logical and actual layout of information warehousing and data discovery structures. The scope of the papers covers the latest and proper subject matters within the parts of organization principles, clustering, net mining, safeguard, facts mining ideas, facts detoxification, functions, info warehouse layout and upkeep, and OLAP. those lawsuits comprise the technical papers chosen for presentation on the convention. We bought greater than a hundred papers from over 20 nations, and this system committee eventually chosen 32 papers. The convention application incorporated one invited speak: “Text Mining functions of a Shallow Parser” via Walter Daelemans, Univer- ty of Antwerp, Belgium. we wish to thank the DEXA 2002 Workshop common Chair (Roland Wagner) th and the organizing committee of the thirteen overseas convention on Database and professional platforms purposes (DEXA 2002) for his or her aid and their cooperation.

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Extra info for Data Warehousing and Knowledge Discovery: 4th International Conference, DaWaK 2002 Aix-en-Provence, France, September 4–6, 2002 Proceedings

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Estimating joint probabilities without combinatory counting. Technical Report TR-764, Computer Science Department,University of Rochester, 2002. 10. C. Silverstein, S. Brin, R. Motwani, and J. Ullman. Scalable techniques for mining causal structures. In Proc. of VLDB, 1998. tw Abstract. In this paper, we devise an efficient algorithm for clustering market-basket data items. Market-basket data analysis has been well addressed in mining association rules for discovering the set of large items which are the frequently purchased items among all transactions.

Ng. The segment support map: Scalable mining of frequent itemsets. SIGKDD Explorations, 2(2), December 2000. 9. Tao Li, Shenghuo Zhu, Mitsunori Ogihara, and Yinhe Cheng. Estimating joint probabilities without combinatory counting. Technical Report TR-764, Computer Science Department,University of Rochester, 2002. 10. C. Silverstein, S. Brin, R. Motwani, and J. Ullman. Scalable techniques for mining causal structures. In Proc. of VLDB, 1998. tw Abstract. In this paper, we devise an efficient algorithm for clustering market-basket data items.

Market-basket data analysis has been well addressed in mining association rules for discovering the set of large items which are the frequently purchased items among all transactions. In essence, clustering is meant to divide a set of data items into some proper groups in such a way that items in the same group are as similar to one another as possible. In view of the nature of clustering market basket data, we present a measurement, called the small-large (SL) ratio, which is in essence the ratio of the number of small items to that of large items.

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