Algorithmic and Complexity Issues of Three Clustering Methods in Microarray Data Analysis

Authors:
Jinsong Tan;Kok Seng Chua;Louxin Zhang;Song Zhu
Affiliations:
Department of Mathematics, National University of Singapore, Singapore, 117543, Singapore;Institute of High Performance Computing, Singapore 117528, Singapore;Department of Mathematics, National University of Singapore, Singapore, 117543, Singapore;ST Electronics (Satcom & Sensor Systems) Pte. Ltd., Singapore 609602, Singapore
Venue:
Algorithmica
Year:
2007

Citing 0
Cited 3

Inapproximability of maximum weighted edge biclique and its applications

TAMC'08 Proceedings of the 5th international conference on Theory and applications of models of computation
Computational systems biology

Algorithms and theory of computation handbook
Parallelized Evolutionary Learning for Detection of Biclusters in Gene Expression Data

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)

Quantified Score

Hi-index	0.00

Visualization

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 fixed. (2) Three variants of the Smooth Clustering problem are NP-hard. The Smooth Subset problem is approximable with ratio 0.5, but it cannot be approximable with ratio 0.5 + δ for any δ 0 unless NP = P. (3) The inferring plaid model problem is NP-hard.