Titlebook: Discovery Science; 10th International C Vincent Corruble,Masayuki Takeda,Einoshin Suzuki Conference proceedings 2007 Springer-Verlag Berlin

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Studies in International Businessreordering the original set of rules according to the error rates obtained on a set of training examples. This is done iteratively, starting from the original set of rules. After obtaining . models these are used as an ensemble for classifying new cases. The net effect of this approach is that the o

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Patrick F. R. Artisien,Peter J. Buckleyem) set mining. Gunopulos et al. [10] have shown that finding a maximum frequent set is .-hard. In this paper I show that the minimization problem is also .-hard. As a next step I investigate whether these problems can be approximated. While a simple greedy algorithm turns out to approximate a minim

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Hafiz Mirza,Peter J. Buckley,John R. Sparkesnts classification, etc. The goal is to efficiently maintain homogenous and well-separated clusters as new data are inserted or existing data are removed. We propose a framework called .based solely on pairwise dissimilarities among all pairs of data and on cluster dominance. In experiments on bench

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Shrawan Kumar,Karl-Hermann Neebng useful information from a huge amount of available content becomes a time consuming process. In this paper, we focus on user modeling for personalization to recommend content relevant to user interests. Techniques used for association rules in deriving user profiles are exploited for discovering

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