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Synergy of two mutations based immune multi-objective automatic fuzzy clustering algorithm

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Abstract

In this paper, a synergy of two mutation based immune multi-objective automatic fuzzy clustering algorithm (STMIMAFC) is proposed for the task of automatically evolving the number of clusters as well as a proper partitioning of data set. In the proposed algorithm, firstly, two new mutation operators, which are designed for the different structures of chromosomes respectively, are cooperated with each other to generate the new individuals. Secondly, we propose an exponential function based compactness validity index. The proposed method has been extensively compared with a synergy of genetic algorithm and multi-objective differential evolution, multi-objective modified differential evolution based fuzzy clustering, multi-objective clustering with automatic \(k\)-determination over a test suit of several real life data sets and synthetic data sets. Experimental results indicate the superiority of the STMIMAFC over other three compared clustering algorithms on clustering accuracy and running time.

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Acknowledgments

The authors would like to thank the editor and the reviewers for helpful comments that greatly improved the paper. This work was supported by the National Natural Science Foundation of China (Nos. 61373111, 61272279, 61103119 and 61203303); the Fundamental Research Funds for the Central Universities (Nos. K50511020014, K5051302084, K50510020011, K5051302049, and K5051302023); and the Provincial Natural Science Foundation of Shaanxi of China (No. 2014JM8321).

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Authors and Affiliations

  1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi’an, 710071, China

    Ruochen Liu, Lang Zhang, Bingjie Li, Yajuan Ma & Licheng Jiao

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  1. Ruochen Liu
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  2. Lang Zhang
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  3. Bingjie Li
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  4. Yajuan Ma
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  5. Licheng Jiao
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Correspondence to Ruochen Liu.

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Liu, R., Zhang, L., Li, B. et al. Synergy of two mutations based immune multi-objective automatic fuzzy clustering algorithm. Knowl Inf Syst 45, 133–157 (2015). https://doi.org/10.1007/s10115-014-0805-4

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