A Genetic Approach to Fuzzy Clustering with a Validity Measure Fitness Function

Susana Nascimento; Fernando Moura Pires

A Genetic Approach to Fuzzy Clustering with a Validity Measure Fitness Function

Susana Nascimento, Fernando Moura Pires

DI - Departamento de Informática

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 Citations (Scopus)

Abstract

This paper presents an extension to the genetic fuzzy clustering algorithm proposed by the authors. The original algorithm, which combines the powerful search technique of genetic algorithms with the fuzzy c-means (FCM) algorithm, is extended such that the FCM algorithm was totally embedded in the genetic operators design. Two objective functions are applied as fitness functions: the performance index of a P fuzzy c-partition Jm(P), used on the FCM algorithm, and the partition coe~cient Fc(P), a function commonly used as a measure of cluster validity. The fuzzy c-means and the new proposal for the genetic fuzzy clustering algorithm were compared on generating multiple prototypes. The experimental results show that the use of genetic search improves the quality of the clustering solutions and that the partition coe~cient Fc(P) is a better measure for clustering than the performance index Jm(P).

Original language	English
Title of host publication	Advances in Intelligent Data Analysis, Reasoning About Data
Editors	X. Liu, P. Cohen, M. Berthold
Publisher	Springer-Verlag
Pages	325-335
Number of pages	11
Volume	1280
Publication status	Published - 1997
Event	Second International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis. Reasoning about Data - London, United Kingdom Duration: 4 Aug 1997 → 4 Aug 1997 http://www.dcs.bbk.ac.uk/archive/ida97/

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer-Verlarg
Volume	1280

Conference

Conference	Second International Symposium on Intelligent Data Analysis
Abbreviated title	IDA-97
Country	United Kingdom
City	London
Period	4/08/97 → 4/08/97
Internet address	http://www.dcs.bbk.ac.uk/archive/ida97/

Keywords

Image segmentation
Information analysis
Algorithms
Data handling
Fuzzy clustering
Fuzzy systems
Genetic algorithms

Cite this

@inproceedings{b95e9173ed2f4896b0396e9858d8fe2b,

title = "A Genetic Approach to Fuzzy Clustering with a Validity Measure Fitness Function",

abstract = "This paper presents an extension to the genetic fuzzy clustering algorithm proposed by the authors. The original algorithm, which combines the powerful search technique of genetic algorithms with the fuzzy c-means (FCM) algorithm, is extended such that the FCM algorithm was totally embedded in the genetic operators design. Two objective functions are applied as fitness functions: the performance index of a P fuzzy c-partition Jm(P), used on the FCM algorithm, and the partition coe~cient Fc(P), a function commonly used as a measure of cluster validity. The fuzzy c-means and the new proposal for the genetic fuzzy clustering algorithm were compared on generating multiple prototypes. The experimental results show that the use of genetic search improves the quality of the clustering solutions and that the partition coe~cient Fc(P) is a better measure for clustering than the performance index Jm(P).",

keywords = "Image segmentation, Information analysis, Algorithms, Data handling, Fuzzy clustering, Fuzzy systems, Genetic algorithms",

author = "Susana Nascimento and {Moura Pires}, Fernando",

year = "1997",

language = "English",

volume = "1280",

series = "Lecture Notes in Computer Science",

publisher = "Springer-Verlag",

pages = "325--335",

editor = "X. Liu and Cohen, {P. } and Berthold, {M. }",

booktitle = "Advances in Intelligent Data Analysis, Reasoning About Data",

note = "Second International Symposium on Intelligent Data Analysis : Advances in Intelligent Data Analysis. Reasoning about Data, IDA-97 ; Conference date: 04-08-1997 Through 04-08-1997",

url = "http://www.dcs.bbk.ac.uk/archive/ida97/",

}

Nascimento, S & Moura Pires, F 1997, A Genetic Approach to Fuzzy Clustering with a Validity Measure Fitness Function. in X Liu, P Cohen & M Berthold (eds), Advances in Intelligent Data Analysis, Reasoning About Data. vol. 1280, Lecture Notes in Computer Science, vol. 1280, Springer-Verlag, pp. 325-335, Second International Symposium on Intelligent Data Analysis, London, United Kingdom, 4/08/97.

A Genetic Approach to Fuzzy Clustering with a Validity Measure Fitness Function. / Nascimento, Susana; Moura Pires, Fernando.

Advances in Intelligent Data Analysis, Reasoning About Data. ed. / X. Liu; P. Cohen; M. Berthold. Vol. 1280 Springer-Verlag, 1997. p. 325-335 (Lecture Notes in Computer Science; Vol. 1280).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Genetic Approach to Fuzzy Clustering with a Validity Measure Fitness Function

AU - Nascimento, Susana

AU - Moura Pires, Fernando

PY - 1997

Y1 - 1997

N2 - This paper presents an extension to the genetic fuzzy clustering algorithm proposed by the authors. The original algorithm, which combines the powerful search technique of genetic algorithms with the fuzzy c-means (FCM) algorithm, is extended such that the FCM algorithm was totally embedded in the genetic operators design. Two objective functions are applied as fitness functions: the performance index of a P fuzzy c-partition Jm(P), used on the FCM algorithm, and the partition coe~cient Fc(P), a function commonly used as a measure of cluster validity. The fuzzy c-means and the new proposal for the genetic fuzzy clustering algorithm were compared on generating multiple prototypes. The experimental results show that the use of genetic search improves the quality of the clustering solutions and that the partition coe~cient Fc(P) is a better measure for clustering than the performance index Jm(P).

AB - This paper presents an extension to the genetic fuzzy clustering algorithm proposed by the authors. The original algorithm, which combines the powerful search technique of genetic algorithms with the fuzzy c-means (FCM) algorithm, is extended such that the FCM algorithm was totally embedded in the genetic operators design. Two objective functions are applied as fitness functions: the performance index of a P fuzzy c-partition Jm(P), used on the FCM algorithm, and the partition coe~cient Fc(P), a function commonly used as a measure of cluster validity. The fuzzy c-means and the new proposal for the genetic fuzzy clustering algorithm were compared on generating multiple prototypes. The experimental results show that the use of genetic search improves the quality of the clustering solutions and that the partition coe~cient Fc(P) is a better measure for clustering than the performance index Jm(P).

KW - Image segmentation

KW - Information analysis

KW - Algorithms

KW - Data handling

KW - Fuzzy clustering

KW - Fuzzy systems

KW - Genetic algorithms

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M3 - Conference contribution

VL - 1280

T3 - Lecture Notes in Computer Science

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EP - 335

BT - Advances in Intelligent Data Analysis, Reasoning About Data

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A2 - Cohen, P.

A2 - Berthold, M.

PB - Springer-Verlag

T2 - Second International Symposium on Intelligent Data Analysis

Y2 - 4 August 1997 through 4 August 1997

ER -

A Genetic Approach to Fuzzy Clustering with a Validity Measure Fitness Function

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Keywords

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