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 Citations (Scopus)

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 1 2008

Fingerprint

Message passing
Cluster Analysis
scaling
knowledge
simulation
experiment
Clustering algorithms
Microarrays
Merging
Gene expression
Gene Expression
Weights and Measures

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

Cite this

Message passing clustering (MPC) : A knowledge-based framework for clustering under biological constraints. / Geng, Huimin; Deng, Xutao; Ali, Hesham H.

In: International Journal of Data Mining and Bioinformatics, Vol. 2, No. 2, 01.06.2008, p. 95-120.

Research output: Contribution to journalArticle

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