Multi-way clustering and biclustering by the Ratio cut and Normalized cut in graphs

Neng Fan, Panos M. Pardalos

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

In this paper, we consider the multi-way clustering problem based on graph partitioning models by the Ratio cut and Normalized cut. We formulate the problem using new quadratic models. Spectral relaxations, new semidefinite programming relaxations and linearization techniques are used to solve these problems. It has been shown that our proposed methods can obtain improved solutions. We also adapt our proposed techniques to the bipartite graph partitioning problem for biclustering.

Original languageEnglish (US)
Pages (from-to)224-251
Number of pages28
JournalJournal of Combinatorial Optimization
Volume23
Issue number2
DOIs
StatePublished - Feb 2012
Externally publishedYes

Fingerprint

Biclustering
Clustering
Graph Partitioning
Graph in graph theory
Linearization
Semidefinite Programming Relaxation
Linearization Techniques
Bipartite Graph
Model

Keywords

  • Biclustering
  • Clustering
  • Graph partitioning
  • Normalized cut
  • Quadratically constrained programming
  • Ratio cut
  • Semidefinite programming
  • Spectral relaxation

ASJC Scopus subject areas

  • Discrete Mathematics and Combinatorics
  • Applied Mathematics
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Control and Optimization

Cite this

Multi-way clustering and biclustering by the Ratio cut and Normalized cut in graphs. / Fan, Neng; Pardalos, Panos M.

In: Journal of Combinatorial Optimization, Vol. 23, No. 2, 02.2012, p. 224-251.

Research output: Contribution to journalArticle

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