Conference Paper

A General Approach to Mining Quality Pattern-Based Clusters from Microarray Data

Simon Fraser University, Burnaby, British Columbia, Canada
DOI: 10.1007/11408079_18 Conference: Database Systems for Advanced Applications, 10th International Conference, DASFAA 2005, Beijing, China, April 17-20, 2005, Proceedings
Source: DBLP


Pattern-based clustering has broad applications in microar- ray 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 interest- ing patterns from the mining results. Moreover, there lacks of a general model for pattern-based clustering. Different kinds of patterns or differ- ent 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.

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    • "Clustering is an important data mining problem (Aggarwal et al., 1999; Aggarwal and Yu, 2000; Cheng et al., 1999; Ester et al., 1996; Pei et al., 2003). For a set of objects, clustering is the process of grouping the objects into a set of disjoint classes, called clusters, such that objects within a cluster have high similarity to each other, while objects in different clusters are dissimilar (Jiang et al., 2005). Recent efforts in data mining have focused on methods for efficient and effective cluster analysis (Zhang et al., 1996) in large databases, e.g., microarray datasets. "
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