A parameter free approach for clustering analysis

Haiqiao Huang, Pik Yin Mok, Yi Lin Kwok, Sau Chuen Joe Au

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

In the paper, we propose a novel parameter free approach for clustering analysis. The approach needs not to make assumptions or define parameters on the cluster number or the results, while the clustered results are visually verified and approved by experimental work. For simplicity, this paper demonstrates the idea using Fuzzy C-Means (FCMs) clustering method, but the proposed open framework allows easy integration with other clustering methods. The method-independent framework generates optimal clustering results and avoids intrinsic biases from individual clustering methods.
Original languageEnglish
Title of host publicationComputer Analysis of Images and Patterns - 13th International Conference, CAIP 2009, Proceedings
Pages816-823
Number of pages8
DOIs
Publication statusPublished - 1 Oct 2009
Event13th International Conference on Computer Analysis of Images and Patterns, CAIP 2009 - Munster, Germany
Duration: 2 Sep 20094 Sep 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5702 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Conference on Computer Analysis of Images and Patterns, CAIP 2009
CountryGermany
CityMunster
Period2/09/094/09/09

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this