A network flow model for biclustering via optimal re-ordering of data matrices

Peter A. Dimaggio, Scott R. McAllister, Christodoulos A. Floudas, Xiao Jiang Feng, Joshua D. Rabinowitz, Herschel A. Rabitz

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

The analysis of large-scale data sets using clustering techniques arises in many different disciplines and has important applications. Most traditional clustering techniques require heuristic methods for finding good solutions and produce suboptimal clusters as a result. In this article, we present a rigorous biclustering approach, OREO, which is based on the Optimal RE-Ordering of the rows and columns of a data matrix. The physical permutations of the rows and columns are accomplished via a network flow model according to a given objective function. This optimal re-ordering model is used in an iterative framework where cluster boundaries in one dimension are used to partition and re-order the other dimensions of the corresponding submatrices. The performance of OREO is demonstrated on metabolite concentration data to validate the ability of the proposed method and compare it to existing clustering methods.

Original languageEnglish (US)
Pages (from-to)343-354
Number of pages12
JournalJournal of Global Optimization
Volume47
Issue number3
DOIs
StatePublished - Jul 1 2010

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Management Science and Operations Research
  • Control and Optimization
  • Applied Mathematics

Keywords

  • Biclustering
  • Mixed-integer linear optimization (MILP)

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