Message passing clustering (MPC): A knowledge-based framework for clustering under biological constraints

Huimin Geng, Xutao Deng, Hesham H. Ali

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

A new clustering algorithm, Message Passing Clustering (MPC), is proposed. MPC employs the concept of message passing to describe parallel and spontaneous clustering process by allowing data objects to communicate with each other. MPC also provides an extensible framework to accommodate additional features into clustering, such as adaptive feature weights scaling, stochastic cluster merging, and semi-supervised constraints guiding. Extensive experiments were performed using both simulation and real microarray gene expression and phylogenetic data. The results showed that MPC performed favourably to other popular clustering algorithms and MPC with the integration of additional features gave even higher accuracy rate than MPC.

Original languageEnglish (US)
Pages (from-to)95-120
Number of pages26
JournalInternational Journal of Data Mining and Bioinformatics
Volume2
Issue number2
DOIs
StatePublished - Jun 2008

Keywords

  • Clustering
  • Clustering algorithms
  • Data mining bioinformatics
  • Feature scaling
  • MPC
  • Message passing clustering
  • Microarray gene expression
  • Phylogenetics
  • Semisupervised
  • Stochastic process

ASJC Scopus subject areas

  • Information Systems
  • Biochemistry, Genetics and Molecular Biology(all)
  • Library and Information Sciences

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