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

Jinsong Tan,Louxin Zhang,Kok Seng Chua

doi:10.1007/11533719_10

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

Jinsong Tan, Louxin Zhang + Show 1 more

https://doi.org/10.1007/11533719_10

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Publication Date: Jan 1, 2005

Citations: 16

Affiliation: National University of Singapore, Institute of High Performance Computing

#Microarray Data Analysis #Clustering Problems + Show 8 more

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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.

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