Finding checkerboard patterns via fractional 0–1 programming

Andrew Trapp (), Oleg A. Prokopyev () and Stanislav Busygin ()
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Andrew Trapp: University of Pittsburgh
Oleg A. Prokopyev: University of Pittsburgh
Stanislav Busygin: University of Florida

Journal of Combinatorial Optimization, 2010, vol. 20, issue 1, No 1, 26 pages

Abstract: Abstract Biclustering is a data mining technique used to simultaneously partition the set of samples and the set of their attributes (features) into subsets (clusters). Samples and features clustered together are supposed to have a high relevance to each other. In this paper we provide a new mathematical programming formulation for unsupervised biclustering. The proposed model involves the solution of a fractional 0–1 programming problem. A linear-mixed 0–1 reformulation as well as two heuristic-based approaches are developed. Encouraging computational results on clustering real DNA microarray data sets are presented. In addition, we also discuss theoretical computational complexity issues related to biclustering.

Keywords: Unsupervised biclustering; Data mining; Fractional 0–1 programming; Heuristics (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10878-008-9186-5

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