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.
Bonizzoni, P., DELLA VEDOVA, G., Dondi, R. (2009). A PTAS for the Minimum Consensus Clustering Problem with a Fixed Number of Clusters. In Proceedings of the 11th Italian Conference on Theoretical Computer Science (ICTCS 2009).
A PTAS for the Minimum Consensus Clustering Problem with a Fixed Number of Clusters
BONIZZONI, PAOLA;DELLA VEDOVA, GIANLUCA;
2009
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.
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