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triCluster: An Effective Algorithm for Mining Coherent Clusters in 3D Microarray Data

Summary: triCluster mines 3D gene-expression triclusters in microarrays with a graph-based approach, supporting positions, overlaps, and patterns. It uses time-slice range multigraphs to extract biclusters via constrained cliques, then links slices to form triclusters. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3739
Venue
SIGMOD
Year
2005
Pagerank
6.513866e-05
Overall Rank
4,775 | 67.25%
DOI
10.1145/1066157.1066236

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{zhao_sigmod05,
        title = {{triCluster: An Effective Algorithm for Mining Coherent Clusters in 3D Microarray Data}},
        author = {Zhao, Lizhuang and Zaki, Mohammed J.},
        series = {{SIGMOD} '05},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1066157.1066236},
        url = {https://dl.acm.org/doi/10.1145/1066157.1066236},
        year = {2005}
}

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