A coevolving memetic algorithm for simultaneous partitional clustering and feature weighting

Yiwen Sun; Zexuan Zhu; Shan He; Zhen Ji

doi:10.1109/MC.2013.6608201

A coevolving memetic algorithm for simultaneous partitional clustering and feature weighting

Yiwen Sun, Zexuan Zhu^*, Shan He, Zhen Ji

^*Corresponding author for this work

Computer Science

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

Abstract

This paper proposes a coevolving Memetic clustering algorithm namely CoMCA for simultaneous partitional clustering and feature weighting. Particularly, CoMCA uses a coevolving particle swarm optimization (PSO) with two swarms for the global search of optimal combination of cluster centroids and feature weights. In each iteration of PSO, a local search based on K-means and gradient descent is introduced to fine-tune the best solution. Comparison study of CoMCA to K-means, PSO clustering, Fuzzy C-means, and WK-Means on test data demonstrates that CoMCA is robust in highlighting relevant features and attaining better (or competitive) performance than the other counterpart algorithms in terms of inter-cluster variance and Rand Index.

Original language	English
Title of host publication	Proceedings of the 2013 IEEE Workshop on Memetic Computing, MC 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
Publisher	IEEE Computer Society
Pages	9-15
Number of pages	7
ISBN (Print)	9781467358910
DOIs	https://doi.org/10.1109/MC.2013.6608201
Publication status	Published - 2013
Event	2013 2nd IEEE Workshop on Memetic Computing, MC 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 - Singapore, Singapore Duration: 16 Apr 2013 → 19 Apr 2013

Publication series

Name	Proceedings of the 2013 IEEE Workshop on Memetic Computing, MC 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013

Conference

Conference	2013 2nd IEEE Workshop on Memetic Computing, MC 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
Country/Territory	Singapore
City	Singapore
Period	16/04/13 → 19/04/13

Bibliographical note

Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications

Access to Document

10.1109/MC.2013.6608201

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Sun, Y., Zhu, Z., He, S., & Ji, Z. (2013). A coevolving memetic algorithm for simultaneous partitional clustering and feature weighting. In Proceedings of the 2013 IEEE Workshop on Memetic Computing, MC 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 (pp. 9-15). Article 6608201 (Proceedings of the 2013 IEEE Workshop on Memetic Computing, MC 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013). IEEE Computer Society. https://doi.org/10.1109/MC.2013.6608201

Sun, Yiwen ; Zhu, Zexuan ; He, Shan et al. / A coevolving memetic algorithm for simultaneous partitional clustering and feature weighting. Proceedings of the 2013 IEEE Workshop on Memetic Computing, MC 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013. IEEE Computer Society, 2013. pp. 9-15 (Proceedings of the 2013 IEEE Workshop on Memetic Computing, MC 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013).

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title = "A coevolving memetic algorithm for simultaneous partitional clustering and feature weighting",

abstract = "This paper proposes a coevolving Memetic clustering algorithm namely CoMCA for simultaneous partitional clustering and feature weighting. Particularly, CoMCA uses a coevolving particle swarm optimization (PSO) with two swarms for the global search of optimal combination of cluster centroids and feature weights. In each iteration of PSO, a local search based on K-means and gradient descent is introduced to fine-tune the best solution. Comparison study of CoMCA to K-means, PSO clustering, Fuzzy C-means, and WK-Means on test data demonstrates that CoMCA is robust in highlighting relevant features and attaining better (or competitive) performance than the other counterpart algorithms in terms of inter-cluster variance and Rand Index.",

author = "Yiwen Sun and Zexuan Zhu and Shan He and Zhen Ji",

note = "Copyright: Copyright 2014 Elsevier B.V., All rights reserved.; 2013 2nd IEEE Workshop on Memetic Computing, MC 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 ; Conference date: 16-04-2013 Through 19-04-2013",

year = "2013",

doi = "10.1109/MC.2013.6608201",

language = "English",

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series = "Proceedings of the 2013 IEEE Workshop on Memetic Computing, MC 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013",

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booktitle = "Proceedings of the 2013 IEEE Workshop on Memetic Computing, MC 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013",

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Sun, Y, Zhu, Z, He, S & Ji, Z 2013, A coevolving memetic algorithm for simultaneous partitional clustering and feature weighting. in Proceedings of the 2013 IEEE Workshop on Memetic Computing, MC 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013., 6608201, Proceedings of the 2013 IEEE Workshop on Memetic Computing, MC 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013, IEEE Computer Society, pp. 9-15, 2013 2nd IEEE Workshop on Memetic Computing, MC 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013, Singapore, Singapore, 16/04/13. https://doi.org/10.1109/MC.2013.6608201

A coevolving memetic algorithm for simultaneous partitional clustering and feature weighting. / Sun, Yiwen; Zhu, Zexuan; He, Shan et al.
Proceedings of the 2013 IEEE Workshop on Memetic Computing, MC 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013. IEEE Computer Society, 2013. p. 9-15 6608201 (Proceedings of the 2013 IEEE Workshop on Memetic Computing, MC 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013).

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

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AB - This paper proposes a coevolving Memetic clustering algorithm namely CoMCA for simultaneous partitional clustering and feature weighting. Particularly, CoMCA uses a coevolving particle swarm optimization (PSO) with two swarms for the global search of optimal combination of cluster centroids and feature weights. In each iteration of PSO, a local search based on K-means and gradient descent is introduced to fine-tune the best solution. Comparison study of CoMCA to K-means, PSO clustering, Fuzzy C-means, and WK-Means on test data demonstrates that CoMCA is robust in highlighting relevant features and attaining better (or competitive) performance than the other counterpart algorithms in terms of inter-cluster variance and Rand Index.

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Sun Y, Zhu Z, He S, Ji Z. A coevolving memetic algorithm for simultaneous partitional clustering and feature weighting. In Proceedings of the 2013 IEEE Workshop on Memetic Computing, MC 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013. IEEE Computer Society. 2013. p. 9-15. 6608201. (Proceedings of the 2013 IEEE Workshop on Memetic Computing, MC 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013). doi: 10.1109/MC.2013.6608201

A coevolving memetic algorithm for simultaneous partitional clustering and feature weighting

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