Fuzzy hierarchical clustering based on fuzzy dissimilarity

Y. Q. Lv, Ka Man Lee

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

1 Citation (Scopus)

Abstract

This paper develops a new fuzzy hierarchical clustering method based on Agglomerative nesting with the introduction of fuzzy dissimilarity. Since normal hierarchical clustering methods only can be applied for real numbers while a set of possible values, fuzzy numbers are gathered in data collection. It's important to find an effective and efficient way for clustering so as to realize the structure of the complex data for decision making. In this research, the trapezoidal fuzzy numbers are selected in this research, and the proposed new hierarchical clustering method can be competent with the existing clustering method with the given set of fuzzy numbers.
Original languageEnglish
Title of host publicationIEEE International Conference on Industrial Engineering and Engineering Management, IEEM2011
Pages1024-1027
Number of pages4
DOIs
Publication statusPublished - 1 Dec 2011
Externally publishedYes
EventIEEE International Conference on Industrial Engineering and Engineering Management, IEEM2011 - Singapore, Singapore
Duration: 6 Dec 20119 Dec 2011

Conference

ConferenceIEEE International Conference on Industrial Engineering and Engineering Management, IEEM2011
CountrySingapore
CitySingapore
Period6/12/119/12/11

Keywords

  • Fuzzy Dissimilarity
  • Fuzzy Hierarchical Clustering
  • Fuzzy Number
  • Graded Mean Integration Representation (GMIR)

ASJC Scopus subject areas

  • Business, Management and Accounting (miscellaneous)
  • Industrial and Manufacturing Engineering
  • Safety, Risk, Reliability and Quality

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