Expectation-MiniMax Approach to Clustering Analysis

Yiu Ming Cheung*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review


This paper proposes a general approach named Expectation-MiniMax (EMM) for clustering analysis without knowing the cluster number. It describes the contrast function of Expectation-Maximization (EM) algorithm by an approximate one with a designable error term. Through adaptively minimizing a specific error term meanwhile maximizing the approximate contrast function, the EMM automatically penalizes all rivals during the competitive learning. Subsequently, the EMM not only includes the Rival Penalized Competitive Learning algorithm (Xu et al. 1993) and its Type A form (Xu 1997) with the new variants developed, but also provides a better alternative way to optimize the EM contrast function with at least two advantages: (1) faster model parameter learning speed, and (2) automatic model-complexity selection capability. We present the general learning procedures of the EMM, and demonstrate its outstanding performance in comparison with the EM.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003
Subtitle of host publicationJoint International Conference ICANN/ICONIP 2003 Istanbul, Turkey, June 26–29, 2003 Proceedings
EditorsOkyay Kaynak, Ethem Alpaydin, Erkki Oja, Lei Xu
PublisherSpringer Berlin Heidelberg
Number of pages8
ISBN (Electronic)9783540449898
ISBN (Print)9783540404088
Publication statusPublished - 18 Jun 2003
EventJoint International Conference ICANN/ICONIP 2003 - Istanbul, Turkey
Duration: 26 Jun 200329 Jun 2003

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceJoint International Conference ICANN/ICONIP 2003

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Cluster Number
  • Seed Point
  • Contrast Function
  • Rival Penalize Competitive Learn
  • Extra Computing


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