Algorithmic and complexity issues of three clustering methods in microarray data analysis

Authors:
Jinsong Tan;Kok Seng Chua;Louxin Zhang
Affiliations:
Department of Mathematics, National University of Singapore, Singapore;The Inst. of High Performance Computing, Singapore;Department of Mathematics, National University of Singapore, Singapore
Venue:
COCOON'05 Proceedings of the 11th annual international conference on Computing and Combinatorics
Year:
2005

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Abstract

The complexity, approximation and algorithmic issues of several clustering problems are studied. These non-traditional clustering problems arise from recent studies in microarray data analysis. We prove the following results. (1) Two variants of the Order-Preserving Submatrix problem are NP-hard. There are polynomial-time algorithms for the Order-Preserving Submatrix Problem when the condition or gene sets are given. (2) The Smooth Subset problem cannot be approximable with ratio 0.5 +δ for any constant δ 0 unless NP=P. (3) Inferring plaid model problem is NP-hard.