Advances in Intelligent Data Analysis: Third International by Marc Sebban, Gilles Richard (auth.), David J. Hand, Joost N.

By Marc Sebban, Gilles Richard (auth.), David J. Hand, Joost N. Kok, Michael R. Berthold (eds.)

This publication constitutes the refereed lawsuits of the 3rd foreign Symposium on clever information research, IDA-99 held in Amsterdam, The Netherlands in August 1999.
The 21 revised complete papers and 23 posters awarded within the booklet have been conscientiously reviewed and chosen from a complete of greater than a hundred submissions. The papers handle all present elements of clever facts research; they're geared up in sections on studying, visualization, category and clustering, integration, purposes and media mining.

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Extra resources for Advances in Intelligent Data Analysis: Third International Symposium, IDA-99 Amsterdam, The Netherlands, August 9–11, 1999 Proceedings

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Machine Learning, pages 197– 227, 1990. [22] R. E. Schapire and Y. Singer. Improved boosting algorithms using confidencerated predictions. In Proceedings of COLT’98, pages 80–91, 1998. Mining Clusters with Association Rules Walter A. J. O. nl Abstract. In this paper we propose a method for extracting clusters in a population of customers, where the only information available is the list of products bought by the individual clients. We use association rules having high confidence to construct a hierarchical sequence of clusters.

If for instance two customers differ in exactly one product, their distance is 2/(k + 3), where k is the number of products bought in common. So their distance decreases as the number of common purchases increases. This allows for judging the distance between customers also in terms of the number of products they bought. The +1 in the denominator of the formula for the distance d is added to deal with the case R = T = ∅, but may also be omitted if one defines d(∅, ∅) = 0. This approach leads to almost the same metric.

Graphical representation of the type of fuzzy partition considered 3. Give an importance degree to each rule: The importance degree associated to Rl will be obtained as follows: G(Rl ) = µAl1 (xl1 ) · . . · µAln (xln ) · µBl (y l ) 4. Obtain a final RB from the preliminary linguistic rule set: This step is the only one differing from the original WM-method. Whilst in that method the rule with the highest importance degree is the only one chosen for each combination of antecedents, in our case we allow the two most important rules in each input subspace —if they exist— to form part of the RB.

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