A PTAS for the Minimum Consensus Clustering Problem with a Fixed Number of Clusters
Data Structures and Algorithms
2009-07-13 v1
Abstract
The Consensus Clustering problem has been introduced as an effective way to analyze the results of different microarray experiments. The problem consists of looking for a partition that best summarizes a set of input partitions (each corresponding to a different microarray experiment) under a simple and intuitive cost function. The problem admits polynomial time algorithms on two input partitions, but is APX-hard on three input partitions. We investigate the restriction of Consensus Clustering when the output partition is required to contain at most k sets, giving a polynomial time approximation scheme (PTAS) while proving the NP-hardness of this restriction.
Cite
@article{arxiv.0907.1840,
title = {A PTAS for the Minimum Consensus Clustering Problem with a Fixed Number of Clusters},
author = {Paola Bonizzoni and Gianluca Della Vedova and Riccardo Dondi},
journal= {arXiv preprint arXiv:0907.1840},
year = {2009}
}