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Fuzzy Data Mining from Multidimensional Databases

By Anne Laurent, Bernadette Bouchon-meunier, A. Doucet, Stephane Gancarski and C. Marsala


. Most of the existing learning systems work on data that are stored in poorly structured files. This approach prevents them from dealing with data from real world, which is often heterogeneous and massive and which requires database management tools. In this article, we propose an original solution to data mining which integrates a fuzzy learning tool that constructs fuzzy decision trees with a multidimensional database management system. 1 Introduction Fuzzy decision tree based methods provide good tools to discover knowledge from data. They are equivalent to a set of if-then rules and are declarative since the classification they propose may be explained. Moreover the use of fuzzy set theory allows the treatment of numerical values in a more natural way. But most existing solutions to construct decision trees use files and it is wellknown that this approach is reasonable only if the amount of data used for knowledge discovery is rather small (e.g. fits in core memory). Often, thes..

Year: 2000
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