Ordering patterns by combining opinions from multiple sources

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Ordering patterns by combining opinions from multiple sources
Pattern ordering is an important task in data mining because the number of patterns extracted by standard data mining algorithms often exceeds our capacity to manually analyze them. In this paper, we present an effective approach to address the pattern ordering problem by combining the rank information gathered from disparate sources. Although rank aggregation techniques have been developed for applications such as meta-search engines, they are not directly applicable to pattern ordering for two reasons. First, the techniques are mostly supervised, i.e., they require a sufficient amount of labeled data. Second, the objects to be ranked are assumed to be independent and identically distributed (i.i.d), an assumption that seldom holds in pattern ordering. The method proposed in this paper is an adaptation of the original Hedge algorithm, modified to work in an unsupervised learning setting. Techniques for addressing the i.i.d. violation in pattern ordering are also presented. Experiment...
Pang-Ning Tan, Rong Jin
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2004
Where KDD
Authors Pang-Ning Tan, Rong Jin
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