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Multilocal Search and Adaptive Niching Based Memetic Algorithm With a Consensus Criterion for Data Clustering

Sheng, Weiguo, Chen, Shengyong, Fairhurst, Michael, Xiao, Gang, Mao, Jiafa (2014) Multilocal Search and Adaptive Niching Based Memetic Algorithm With a Consensus Criterion for Data Clustering. IEEE Transactions on Evolutionary Computation, 18 (5). pp. 721-741. ISSN 1089-778X. (doi:10.1109/TEVC.2013.2283513) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:43686)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://dx.doi.org/10.1109/TEVC.2013.2283513

Abstract

Clustering is deemed one of the most difficult and challenging problems in machine learning. In this paper, we propose a multilocal search and adaptive niching-based genetic algorithm with a consensus criterion for automatic data clustering. The proposed algorithm employs three local searches of different features in a sophisticated manner to efficiently exploit the decision space. Furthermore, we develop an adaptive niching method, which can dynamically adjust its parameter value depending on the problem instance as well as the search progress, and incorporate it into the proposed algorithm. The adaptation strategy is based on a newly devised population diversity index, which can be used to promote both genetic diversity and fitness. Consequently, diverged niches of high fitness can be formed and maintained in the population, making the approach well-suited to effective exploration of the complex decision space of clustering problems. The resulting algorithm has been used to optimize a consensus clustering criterion, which is suggested with the purpose of achieving reliable solutions. To evaluate the proposed algorithm, we have conducted a series of experiments on both synthetic and real data and compared it with other reported methods. The results show that our proposed algorithm can achieve superior performance, outperforming related methods.

Item Type: Article
DOI/Identification number: 10.1109/TEVC.2013.2283513
Subjects: T Technology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Tina Thompson
Date Deposited: 24 Oct 2014 15:38 UTC
Last Modified: 17 Aug 2022 10:57 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/43686 (The current URI for this page, for reference purposes)
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