Message passing clustering with stochastic merging based on kernel functions

Huimin Geng, Xutao Deng, Hesham H. Ali

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this paper, we propose a new Stochastic Message Passing Clustering (SMPC) algorithm for clustering biological data based on the Message Passing Clustering (MPC) algorithm, which we introduced in earlier work. MPC has shown its advantage when applied to describing parallel and spontaneous biological processes. SMPC, as a generalized version of MPC, extends the clustering algorithm from a deterministic process to a stochastic process, adding three major advantages. First, in deciding the merging cluster pair, the influences of all clusters are quantified by probabilities, estimated by kernel functions based on their relative distances. Second, the proposed algorithm property resolve the "tie" problem, which often occurs for integer distances as in the case of protein interaction data. Third, clustering can be undone to improve the clustering performance when the algorithm detects objects which don't have good probabilities inside the cluster and moves them outside. The test results on colon cancer gene-expression data show that SMPC performs better than the deterministic MPC.

Original languageEnglish (US)
Title of host publicationAssociation for Information Systems - 11th Americas Conference on Information Systems, AMCIS 2005
Subtitle of host publicationA Conference on a Human Scale
Pages2662-2671
Number of pages10
StatePublished - 2005
Event11th Americas Conference on Information Systems, AMCIS 2005 - Omaha, NE, United States
Duration: Aug 11 2005Aug 15 2005

Publication series

NameAssociation for Information Systems - 11th Americas Conference on Information Systems, AMCIS 2005: A Conference on a Human Scale
Volume6

Conference

Conference11th Americas Conference on Information Systems, AMCIS 2005
CountryUnited States
CityOmaha, NE
Period8/11/058/15/05

Keywords

  • Clustering
  • Hierarchical clustering
  • Kernel functions
  • Message passing clustering
  • Stochastic processes

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Networks and Communications
  • Information Systems
  • Library and Information Sciences

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    Geng, H., Deng, X., & Ali, H. H. (2005). Message passing clustering with stochastic merging based on kernel functions. In Association for Information Systems - 11th Americas Conference on Information Systems, AMCIS 2005: A Conference on a Human Scale (pp. 2662-2671). (Association for Information Systems - 11th Americas Conference on Information Systems, AMCIS 2005: A Conference on a Human Scale; Vol. 6).