### Abstract

Computing a suitable measure of consensus among several clusterings on the same data is an important problem that arises in several areas such as computational biology and data mining. In this paper, we formalize a set-theoretic model for computing such a similarity measure. Roughly speaking, in this model we have k > 1 partitions (clusters) of the same data set each containing the same number of sets and the goal is to align the sets in each partition to minimize a similarity measure. For k = 2, a polynomial-time solution was proposed by Gusfield (Information Processing Letters 82 (2002) 159-164). In this paper, we show that the problem is MAX-SNP-hard for k = 3 even if each partition in each cluster contains no more than 2 elements and provide a 2 - frac(2, k)-approximation algorithm for the problem for any k.

Original language | English (US) |
---|---|

Pages (from-to) | 137-145 |

Number of pages | 9 |

Journal | Information Processing Letters |

Volume | 104 |

Issue number | 4 |

DOIs | |

State | Published - Nov 15 2007 |

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### All Science Journal Classification (ASJC) codes

- Theoretical Computer Science
- Signal Processing
- Information Systems
- Computer Science Applications

### Cite this

*Information Processing Letters*,

*104*(4), 137-145. https://doi.org/10.1016/j.ipl.2007.06.008

}

*Information Processing Letters*, vol. 104, no. 4, pp. 137-145. https://doi.org/10.1016/j.ipl.2007.06.008

**On constructing an optimal consensus clustering from multiple clusterings.** / Berman, Piotr; DasGupta, Bhaskar; Kao, Ming Yang; Wang, Jie.

Research output: Contribution to journal › Article

TY - JOUR

T1 - On constructing an optimal consensus clustering from multiple clusterings

AU - Berman, Piotr

AU - DasGupta, Bhaskar

AU - Kao, Ming Yang

AU - Wang, Jie

PY - 2007/11/15

Y1 - 2007/11/15

N2 - Computing a suitable measure of consensus among several clusterings on the same data is an important problem that arises in several areas such as computational biology and data mining. In this paper, we formalize a set-theoretic model for computing such a similarity measure. Roughly speaking, in this model we have k > 1 partitions (clusters) of the same data set each containing the same number of sets and the goal is to align the sets in each partition to minimize a similarity measure. For k = 2, a polynomial-time solution was proposed by Gusfield (Information Processing Letters 82 (2002) 159-164). In this paper, we show that the problem is MAX-SNP-hard for k = 3 even if each partition in each cluster contains no more than 2 elements and provide a 2 - frac(2, k)-approximation algorithm for the problem for any k.

AB - Computing a suitable measure of consensus among several clusterings on the same data is an important problem that arises in several areas such as computational biology and data mining. In this paper, we formalize a set-theoretic model for computing such a similarity measure. Roughly speaking, in this model we have k > 1 partitions (clusters) of the same data set each containing the same number of sets and the goal is to align the sets in each partition to minimize a similarity measure. For k = 2, a polynomial-time solution was proposed by Gusfield (Information Processing Letters 82 (2002) 159-164). In this paper, we show that the problem is MAX-SNP-hard for k = 3 even if each partition in each cluster contains no more than 2 elements and provide a 2 - frac(2, k)-approximation algorithm for the problem for any k.

UR - http://www.scopus.com/inward/record.url?scp=34548032601&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34548032601&partnerID=8YFLogxK

U2 - 10.1016/j.ipl.2007.06.008

DO - 10.1016/j.ipl.2007.06.008

M3 - Article

AN - SCOPUS:34548032601

VL - 104

SP - 137

EP - 145

JO - Information Processing Letters

JF - Information Processing Letters

SN - 0020-0190

IS - 4

ER -