ACM Computing Surveys (CSUR)
Clustering by pattern similarity in large data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
OP-Cluster: Clustering by Tendency in High Dimensional Space
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
MaPle: A Fast Algorithm for Maximal Pattern-based Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Interactive exploration of coherent patterns in time-series gene expression data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining coherent gene clusters from gene-sample-time microarray data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining top-K covering rule groups for gene expression data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
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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.