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COCOON
2005
Springer

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

14 years 6 months ago
Algorithmic and Complexity Issues of Three Clustering Methods in Microarray Data Analysis
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 OrderPreserving 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) Inferring plaid model problem is NP-hard.
Jinsong Tan, Kok Seng Chua, Louxin Zhang
Added 26 Jun 2010
Updated 26 Jun 2010
Type Conference
Year 2005
Where COCOON
Authors Jinsong Tan, Kok Seng Chua, Louxin Zhang
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