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MJ: TRICLUSTER: an effective algorithm for mining coherent clusters in 3Dmicroarray data
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| Venue: | In SIGMOD ’05: Proceedings of the 2005 ACM SIGMOD international |
| Citations: | 38 - 2 self |
BibTeX
@INPROCEEDINGS{Zhao_mj:tricluster:,
author = {Lizhuang Zhao and Mohammed J. Zaki},
title = {MJ: TRICLUSTER: an effective algorithm for mining coherent clusters in 3Dmicroarray data},
booktitle = {In SIGMOD ’05: Proceedings of the 2005 ACM SIGMOD international},
year = {}
}
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Abstract
In this paper we introduce a novel algorithm called tri-Cluster, for mining coherent clusters in three-dimensional (3D) gene expression datasets. triCluster can mine arbi-trarily positioned and overlapping clusters, and depending on di®erent parameter values, it can mine di®erent types of clusters, including those with constant or similar values along each dimension, as well as scaling and shifting ex-pression patterns. triCluster relies on graph-based ap-proach to mine all valid clusters. For each time slice, i.e., a gene£sample matrix, it constructs the range multigraph, a compact representation of all similar value ranges between any two sample columns. It then searches for constrained maximal cliques in this multigraph to yield the set of bi-clusters for this time slice. Then triCluster constructs another graph using the biclusters (as vertices) from each time slice; mining cliques from this graph yields the ¯nal set of triclusters. Optionally, triCluster merges/deletes some clusters having large overlaps. We present a useful set of metrics to evaluate the clustering quality, and we show that triCluster can ¯nd signi¯cant triclusters in the real microarray datasets. 1.
Keyphrases
coherent cluster effective algorithm time slice similar value nal set tricluster construct ex-pression pattern di erent parameter value range multigraph signi cant triclusters novel algorithm sample column gene expression datasets clustering quality gene sample matrix useful set tricluster merges large overlap real microarray datasets tricluster relies maximal clique graph-based ap-proach valid cluster compact representation di erent type
