Fuzzy hierarchical clustering based on fuzzy dissimilarity

Y. Q. Lv; Ka Man Lee

doi:10.1109/IEEM.2011.6118070

Fuzzy hierarchical clustering based on fuzzy dissimilarity

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

3 Citations (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 language	English
Title of host publication	IEEE International Conference on Industrial Engineering and Engineering Management, IEEM2011
Pages	1024-1027
Number of pages	4
DOIs	https://doi.org/10.1109/IEEM.2011.6118070
Publication status	Published - 1 Dec 2011
Externally published	Yes
Event	IEEE International Conference on Industrial Engineering and Engineering Management, IEEM2011 - Singapore, Singapore Duration: 6 Dec 2011 → 9 Dec 2011

Conference

Conference	IEEE International Conference on Industrial Engineering and Engineering Management, IEEM2011
Country/Territory	Singapore
City	Singapore
Period	6/12/11 → 9/12/11

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

More information

10.1109/IEEM.2011.6118070

Cite this

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title = "Fuzzy hierarchical clustering based on fuzzy dissimilarity",

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.",

keywords = "Fuzzy Dissimilarity, Fuzzy Hierarchical Clustering, Fuzzy Number, Graded Mean Integration Representation (GMIR)",

author = "Lv, {Y. Q.} and Lee, {Ka Man}",

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doi = "10.1109/IEEM.2011.6118070",

language = "English",

isbn = "9781457707391",

pages = "1024--1027",

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note = "IEEE International Conference on Industrial Engineering and Engineering Management, IEEM2011 ; Conference date: 06-12-2011 Through 09-12-2011",

}

Lv, YQ & Lee, KM 2011, Fuzzy hierarchical clustering based on fuzzy dissimilarity. in IEEE International Conference on Industrial Engineering and Engineering Management, IEEM2011., 6118070, pp. 1024-1027, IEEE International Conference on Industrial Engineering and Engineering Management, IEEM2011, Singapore, Singapore, 6/12/11. https://doi.org/10.1109/IEEM.2011.6118070

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N2 - 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.

AB - 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.

KW - Fuzzy Dissimilarity

KW - Fuzzy Hierarchical Clustering

KW - Fuzzy Number

KW - Graded Mean Integration Representation (GMIR)

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DO - 10.1109/IEEM.2011.6118070

M3 - Conference article published in proceeding or book

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BT - IEEE International Conference on Industrial Engineering and Engineering Management, IEEM2011

T2 - IEEE International Conference on Industrial Engineering and Engineering Management, IEEM2011

Y2 - 6 December 2011 through 9 December 2011

ER -

Fuzzy hierarchical clustering based on fuzzy dissimilarity

Abstract

Conference

Keywords

ASJC Scopus subject areas

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