Memetic algorithm based fuzzy clustering

Anh Duc Do; Siu Yeung Cho

doi:10.1109/CEC.2007.4424771

Memetic algorithm based fuzzy clustering

Anh Duc Do^*
, Siu Yeung Cho

^*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

This paper presents a Memetic Algorithm (MA) based Fuzzy C-Means (FCM) clustering algorithm. Traditional FCM algorithm suffers from the problem of local optimal, whereas the proposed MA-based FCM algorithm is able to overcome this problem and produce good performance in various ways. Experimental results showed that the proposed clustering algorithm outperforms traditional fuzzy clustering algorithms significantly on a wide variety of datasets with overlapping class boundaries and spread data distributions.

Original language	English
Title of host publication	2007 IEEE Congress on Evolutionary Computation, CEC 2007
Publisher	IEEE Computer Society
Pages	2398-2404
Number of pages	7
ISBN (Print)	1424413400, 9781424413409
DOIs	https://doi.org/10.1109/CEC.2007.4424771
Publication status	Published - 2007
Externally published	Yes
Event	2007 IEEE Congress on Evolutionary Computation, CEC 2007 - Singapore, Singapore Duration: 25 Sept 2007 → 28 Sept 2007

Publication series

Name	2007 IEEE Congress on Evolutionary Computation, CEC 2007

Conference

Conference	2007 IEEE Congress on Evolutionary Computation, CEC 2007
Country/Territory	Singapore
City	Singapore
Period	25/09/07 → 28/09/07

ASJC Scopus subject areas

Artificial Intelligence
Software
Theoretical Computer Science

Access to Document

10.1109/CEC.2007.4424771

Cite this

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title = "Memetic algorithm based fuzzy clustering",

abstract = "This paper presents a Memetic Algorithm (MA) based Fuzzy C-Means (FCM) clustering algorithm. Traditional FCM algorithm suffers from the problem of local optimal, whereas the proposed MA-based FCM algorithm is able to overcome this problem and produce good performance in various ways. Experimental results showed that the proposed clustering algorithm outperforms traditional fuzzy clustering algorithms significantly on a wide variety of datasets with overlapping class boundaries and spread data distributions.",

author = "Do, \{Anh Duc\} and Cho, \{Siu Yeung\}",

year = "2007",

doi = "10.1109/CEC.2007.4424771",

language = "English",

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series = "2007 IEEE Congress on Evolutionary Computation, CEC 2007",

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address = "United States",

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AU - Cho, Siu Yeung

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N2 - This paper presents a Memetic Algorithm (MA) based Fuzzy C-Means (FCM) clustering algorithm. Traditional FCM algorithm suffers from the problem of local optimal, whereas the proposed MA-based FCM algorithm is able to overcome this problem and produce good performance in various ways. Experimental results showed that the proposed clustering algorithm outperforms traditional fuzzy clustering algorithms significantly on a wide variety of datasets with overlapping class boundaries and spread data distributions.

AB - This paper presents a Memetic Algorithm (MA) based Fuzzy C-Means (FCM) clustering algorithm. Traditional FCM algorithm suffers from the problem of local optimal, whereas the proposed MA-based FCM algorithm is able to overcome this problem and produce good performance in various ways. Experimental results showed that the proposed clustering algorithm outperforms traditional fuzzy clustering algorithms significantly on a wide variety of datasets with overlapping class boundaries and spread data distributions.

UR - https://www.scopus.com/pages/publications/79955344486

U2 - 10.1109/CEC.2007.4424771

DO - 10.1109/CEC.2007.4424771

M3 - Conference contribution

AN - SCOPUS:79955344486

SN - 1424413400

SN - 9781424413409

T3 - 2007 IEEE Congress on Evolutionary Computation, CEC 2007

SP - 2398

EP - 2404

BT - 2007 IEEE Congress on Evolutionary Computation, CEC 2007

PB - IEEE Computer Society

T2 - 2007 IEEE Congress on Evolutionary Computation, CEC 2007

Y2 - 25 September 2007 through 28 September 2007

ER -

Memetic algorithm based fuzzy clustering

Abstract

Publication series

Conference

ASJC Scopus subject areas

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