### Abstract

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 language | English (US) |
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Title of host publication | Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011 |

Pages | 884-895 |

Number of pages | 12 |

State | Published - Dec 1 2011 |

Event | 11th SIAM International Conference on Data Mining, SDM 2011 - Mesa, AZ, United States Duration: Apr 28 2011 → Apr 30 2011 |

### Publication series

Name | Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011 |
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### Other

Other | 11th SIAM International Conference on Data Mining, SDM 2011 |
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Country | United States |

City | Mesa, AZ |

Period | 4/28/11 → 4/30/11 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Software

### Cite this

*Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011*(pp. 884-895). (Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011).

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*Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011.*Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011, pp. 884-895, 11th SIAM International Conference on Data Mining, SDM 2011, Mesa, AZ, United States, 4/28/11.

**Exemplar-based robust coherent biclustering.** / Tu, Kewei; Ouyang, Xixiu; Han, Dingyi; Yu, Yong; Honavar, Vasant.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Exemplar-based robust coherent biclustering

AU - Tu, Kewei

AU - Ouyang, Xixiu

AU - Han, Dingyi

AU - Yu, Yong

AU - Honavar, Vasant

PY - 2011/12/1

Y1 - 2011/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84880102718&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84880102718&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84880102718

SN - 9780898719925

T3 - Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011

SP - 884

EP - 895

BT - Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011

ER -