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Erschienen in:
2005 | OriginalPaper | Buchkapitel
A General Approach to Mining Quality Pattern-Based Clusters from Microarray Data
verfasst von : Daxin Jiang, Jian Peii, Aidong Zhang
Erschienen in: Database Systems for Advanced Applications
Verlag: Springer Berlin Heidelberg
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Pattern-based clustering has broad applications in microarray data analysis, customer segmentation, e-business data analysis, etc. However, pattern-based clustering often returns a large number of highly-overlapping clusters, which makes it hard for users to identify interesting patterns from the mining results. Moreover, there lacks of a general model for pattern-based clustering. Different kinds of patterns or different measures on the pattern coherence may require different algorithms. In this paper, we address the above two problems by proposing a general quality-driven approach to mining top-
k
quality pattern-based clusters. We examine our quality-driven approach using real world microarray data sets. The experimental results show that our method is general, effective and efficient.