By Marco Botta, Jean-Francois Boulicaut, Cyrille Masson, Rosa Meo (auth.), Yahiko Kambayashi, Werner Winiwarter, Masatoshi Arikawa (eds.)
Within the previous couple of years facts Warehousing and data Discovery expertise has confirmed itself as a key expertise for organisations that desire to enhance the standard of the implications received from info research, determination aid, and the automated extraction of information from info. The Fourth overseas convention on info Warehousing and data Discovery (DaWaK 2002) keeps a chain of winning meetings devoted to this subject. Its major goal is to compile researchers and practitioners to debate examine matters and event in constructing and deploying facts warehousing and data discovery platforms, purposes, and suggestions. The convention specializes in the logical and actual layout of knowledge warehousing and information discovery platforms. The scope of the papers covers the latest and appropriate subject matters within the parts of organization ideas, clustering, net mining, safety, information mining suggestions, information detoxification, functions, information warehouse layout and upkeep, and OLAP. those lawsuits include the technical papers chosen for presentation on the convention. We acquired greater than a hundred papers from over 20 nations, and this system committee eventually chosen 32 papers. The convention software integrated one invited speak: “Text Mining functions of a Shallow Parser” by means of Walter Daelemans, Univer- ty of Antwerp, Belgium. we want to thank the DEXA 2002 Workshop basic Chair (Roland Wagner) th and the organizing committee of the thirteen overseas convention on Database and specialist platforms purposes (DEXA 2002) for his or her aid and their cooperation.
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Additional resources for Data Warehousing and Knowledge Discovery: 4th International Conference, DaWaK 2002 Aix-en-Provence, France, September 4–6, 2002 Proceedings
Lakshmanan, C. K. S. Leung, and R. T. 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.
We would also like to thank Dr. Meng Xiang Tang and Xianghui Liu for their helpful discussions. Y. Kambayashi, W. Winiwarter, M. ): DaWaK 2002, LNCS 2454, pp. 31–41, 2002. c Springer-Verlag Berlin Heidelberg 2002 32 T. Li et al. or space complexity of combinatory counting is very large even unacceptable. For example, in a large dataset which cannot ﬁt in the main memory, combinatory counting would incur considerable overheads. Even in cases in which the complexity of combinatory counting is manageable, there can also be reasons to consider the estimation methods.
Although the ﬁrst algorithms assumed that transactions were represented as sets of binary attributes, along the years many algorithms for the extraction of ARs from multivariate data have been proposed. , [7,17]). , [6,15]), or to semi-structured data . However, as far as we know, ARs within the context of native XML documents have never been proposed. To assist association rule mining, a number of query languages have been proposed. The MINE RULE operator, used in this paper, was ﬁrst introduced in  and extended in .
Data Warehousing and Knowledge Discovery: 4th International Conference, DaWaK 2002 Aix-en-Provence, France, September 4–6, 2002 Proceedings by Marco Botta, Jean-Francois Boulicaut, Cyrille Masson, Rosa Meo (auth.), Yahiko Kambayashi, Werner Winiwarter, Masatoshi Arikawa (eds.)