Exemplar-based robust coherent biclustering

Kewei Tu, Xixiu Ouyang, Dingyi Han, Yong Yu, Vasant Honavar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations


The biclustering, co-clustering, or subspace clustering problem involves simultaneously grouping the rows and columns of a data matrix to uncover biclusters or sub-matrices of the data matrix that optimize a desired objective function. In coherent biclustering, the objective function contains a coherence measure of the biclusters. We introduce a novel formulation of the coherent biclustering problem and use it to derive two algorithms. The first algorithm is based on loopy message passing; and the second relies on a greedy strategy yielding an algorithm that is significantly faster than the first. A distinguishing feature of these algorithms is that they identify an exemplar or a prototypical member of each bicluster. We note the interference from background elements in biclustering, and offer a means to circumvent such interference using additional regularization. Our experiments with synthetic as well as real-world datasets show that our algorithms are competitive with the current state-of-the-art algorithms for finding coherent biclusters.

Original languageEnglish (US)
Title of host publicationProceedings of the 11th SIAM International Conference on Data Mining, SDM 2011
PublisherSociety for Industrial and Applied Mathematics Publications
Number of pages12
ISBN (Print)9780898719925
StatePublished - 2011
Event11th SIAM International Conference on Data Mining, SDM 2011 - Mesa, AZ, United States
Duration: Apr 28 2011Apr 30 2011

Publication series

NameProceedings of the 11th SIAM International Conference on Data Mining, SDM 2011


Other11th SIAM International Conference on Data Mining, SDM 2011
Country/TerritoryUnited States
CityMesa, AZ

All Science Journal Classification (ASJC) codes

  • Software


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