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Proceedings of the Seventh SIAM International Conference on Data Mining

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2007 / xiv+648 pages / Softcover / ISBN: 978-0-898716-30-6 / List Price: $197.00 / SIAM Member Price: $137.90 / Order Code: PR127

Edited by Chid Apte, Bing Liu, Srinivasan Parthasarathy, and David Skillicorn

Minneapolis, MN
April 26-28, 2007

The Seventh SIAM International Conference on Data Mining (SDM 2007) continues a series of conferences whose focus is the theory and application of data mining to complex datasets in science, engineering, biomedicine, and the social sciences. These datasets challenge our abilities to analyze them because they are large and often noisy. Sophisticated, high-performance, and principled analysis techniques and algorithms, based on sound statistical foundations, are required. Visualization is often critically important; tuning for performance is a significant challenge; and the appropriate levels of abstraction to allow end-users to exploit sophisticated techniques and understand clearly both the constraints and interpretation of results are still something of an open question.

This year the conference received a record number of papers (302, as compared to 242 last year). Each submitted paper was reviewed initially by at least three members of the international program committee. Area chairs then initiated discussion on papers with discrepant scores and subsequently provided their recommendations to the program co-chairs, who then collated and refined these suggestions across all areas. In the end, 36 papers were selected to appear as full papers, and 39 papers were selected as short papers or posters. We believe that the hard work of all the authors, reviewers, and area chairs has resulted in an excellent set of papers that will be valuable both to researchers and practitioners in data mining for many years to come.

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