A randomized PTAS for the minimum Consensus Clustering with a fixed number of clusters

Abstract: The Consensus Clustering problem has been introduced as an effective way to analyze the results of different microarray experiments (Filkov and Skiena (2004a,b) . The problem asks for a partition that summarizes a set of input partitions (each corresponding to a different microarray experiment) under a simple and intuitive cost. The problem on instances with two input partitions has a simple polynomial time algorithm, but it becomes APX-hard on instances with three input partitions. The quest for defining the boundary between tractable and intractable instances leads to the investigation of the restriction of Consensus Clustering when the output partition contains a fixed number of sets. In this paper, we give a randomized polynomial time approximation scheme for such problems, while proving its NP-hardness even for 2 output partitions, therefore definitively settling the approximation complexity of the problem. [Copyright &y& Elsevier]