A fuzzy clustering algorithm based on artificial immune principles

Liu Furong; X. Z. Gao; Wang Changhong; Wang Qiaoling

doi:10.1109/CIS.2007.22

A fuzzy clustering algorithm based on artificial immune principles

Liu Furong^*
, X. Z. Gao
, Wang Changhong
, Wang Qiaoling

^*Corresponding author for this work

School of Astronautics

Harbin Institute of Technology
Aalto University

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

1 Scopus citations

Abstract

The Fuzzy C-Means algorithm (FCM) is a widely applied clustering method. However, it is usually trapped into the local optimum. In addition, its performance is very sensitive to the initialization. This paper proposes a new fuzzy clustering method based on the immune clonal selection principle, namely CFCM. The clonal selection algorithm is first used to optimize the number of fuzzy cluster centers. The FCM is next employed for clustering the input data. Simulation results demonstrate that our novel approach can overcome the drawbacks of the regular FCM with an improved data clustering performance.

Original language	English
Title of host publication	Proceedings - 2007 International Conference on Computational Intelligence and Security, CIS 2007
Pages	475-479
Number of pages	5
DOIs	https://doi.org/10.1109/CIS.2007.22
State	Published - 2007
Event	2007 International Conference on Computational Intelligence and Security, CIS'07 - Harbin, Heilongjiang, China Duration: 15 Dec 2007 → 19 Dec 2007

Publication series

Name	Proceedings - 2007 International Conference on Computational Intelligence and Security, CIS 2007

Conference

Conference	2007 International Conference on Computational Intelligence and Security, CIS'07
Country/Territory	China
City	Harbin, Heilongjiang
Period	15/12/07 → 19/12/07

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10.1109/CIS.2007.22

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Furong, L., Gao, X. Z., Changhong, W., & Qiaoling, W. (2007). A fuzzy clustering algorithm based on artificial immune principles. In Proceedings - 2007 International Conference on Computational Intelligence and Security, CIS 2007 (pp. 475-479). Article 4415389 (Proceedings - 2007 International Conference on Computational Intelligence and Security, CIS 2007). https://doi.org/10.1109/CIS.2007.22

@inproceedings{d2b3300fe3bc44e89dc2400019384bd2,

title = "A fuzzy clustering algorithm based on artificial immune principles",

abstract = "The Fuzzy C-Means algorithm (FCM) is a widely applied clustering method. However, it is usually trapped into the local optimum. In addition, its performance is very sensitive to the initialization. This paper proposes a new fuzzy clustering method based on the immune clonal selection principle, namely CFCM. The clonal selection algorithm is first used to optimize the number of fuzzy cluster centers. The FCM is next employed for clustering the input data. Simulation results demonstrate that our novel approach can overcome the drawbacks of the regular FCM with an improved data clustering performance.",

author = "Liu Furong and Gao, \{X. Z.\} and Wang Changhong and Wang Qiaoling",

year = "2007",

doi = "10.1109/CIS.2007.22",

language = "英语",

isbn = "0769530729",

series = "Proceedings - 2007 International Conference on Computational Intelligence and Security, CIS 2007",

pages = "475--479",

booktitle = "Proceedings - 2007 International Conference on Computational Intelligence and Security, CIS 2007",

note = "2007 International Conference on Computational Intelligence and Security, CIS'07 ; Conference date: 15-12-2007 Through 19-12-2007",

}

Furong, L, Gao, XZ, Changhong, W & Qiaoling, W 2007, A fuzzy clustering algorithm based on artificial immune principles. in Proceedings - 2007 International Conference on Computational Intelligence and Security, CIS 2007., 4415389, Proceedings - 2007 International Conference on Computational Intelligence and Security, CIS 2007, pp. 475-479, 2007 International Conference on Computational Intelligence and Security, CIS'07, Harbin, Heilongjiang, China, 15/12/07. https://doi.org/10.1109/CIS.2007.22

A fuzzy clustering algorithm based on artificial immune principles. / Furong, Liu; Gao, X. Z.; Changhong, Wang et al.
Proceedings - 2007 International Conference on Computational Intelligence and Security, CIS 2007. 2007. p. 475-479 4415389 (Proceedings - 2007 International Conference on Computational Intelligence and Security, CIS 2007).

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

TY - GEN

T1 - A fuzzy clustering algorithm based on artificial immune principles

AU - Furong, Liu

AU - Gao, X. Z.

AU - Changhong, Wang

AU - Qiaoling, Wang

PY - 2007

Y1 - 2007

N2 - The Fuzzy C-Means algorithm (FCM) is a widely applied clustering method. However, it is usually trapped into the local optimum. In addition, its performance is very sensitive to the initialization. This paper proposes a new fuzzy clustering method based on the immune clonal selection principle, namely CFCM. The clonal selection algorithm is first used to optimize the number of fuzzy cluster centers. The FCM is next employed for clustering the input data. Simulation results demonstrate that our novel approach can overcome the drawbacks of the regular FCM with an improved data clustering performance.

AB - The Fuzzy C-Means algorithm (FCM) is a widely applied clustering method. However, it is usually trapped into the local optimum. In addition, its performance is very sensitive to the initialization. This paper proposes a new fuzzy clustering method based on the immune clonal selection principle, namely CFCM. The clonal selection algorithm is first used to optimize the number of fuzzy cluster centers. The FCM is next employed for clustering the input data. Simulation results demonstrate that our novel approach can overcome the drawbacks of the regular FCM with an improved data clustering performance.

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U2 - 10.1109/CIS.2007.22

DO - 10.1109/CIS.2007.22

M3 - 会议稿件

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SN - 0769530729

SN - 9780769530727

T3 - Proceedings - 2007 International Conference on Computational Intelligence and Security, CIS 2007

SP - 475

EP - 479

BT - Proceedings - 2007 International Conference on Computational Intelligence and Security, CIS 2007

T2 - 2007 International Conference on Computational Intelligence and Security, CIS'07

Y2 - 15 December 2007 through 19 December 2007

ER -

A fuzzy clustering algorithm based on artificial immune principles

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