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Intuitionistic fuzzy c-means clustering algorithm based on a novel weighted proximity measure and genetic algorithm

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Abstract

In the era of big data, the research on clustering technologies is a popular topic because they can discover the structure of complex data sets with minimal prior knowledge. Among the existing soft clustering technologies, as an extension of fuzzy c-means (FCM) algorithm, the intuitionistic FCM (IFCM) algorithm has been widely used due to its superiority in reducing the effects of outliers/noise and improving the clustering accuracy. In the existing IFCM algorithm, the measurement of proximity degree between a pair of objects and the determination of parameters are two critical problems, which have considerable effects on the clustering results. Therefore, we propose an improved IFCM clustering technique in this paper. Firstly, a novel weighted proximity measure, which aggregates weighted similarity and correlation measures, is proposed to evaluate not only the closeness degree but also the linear relationship between two objects. Subsequently, genetic algorithms are utilized for identifying the optimal parameters. Lastly, experiments on the proposed IFCM technique are conducted on synthetic and UCI data sets. Comparisons with other approaches in cluster evaluation indexes indicate the effectiveness and superiority of our method.

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Acknowledgements

The authors are very grateful to the anonymous referees for their valuable comments and suggestions. This work was supported by the National Natural Science Foundation of China (no. 71871228).

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Authors and Affiliations

  1. School of Business, Central South University, Changsha, 410083, People’s Republic of China

    Wen-hui Hou, Yi-ting Wang & Jian-qiang Wang

  2. Hunan Engineering Research Center for Intelligent Decision Making and Big Data on Industrial Development, Hunan University of Science and Technology, Xiangtan, 411201, People’s Republic of China

    Jian-qiang Wang & Peng-Fei Cheng

  3. School of Business, Hunan University, Changsha, 410082, People’s Republic of China

    Lin Li

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  1. Wen-hui Hou
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  2. Yi-ting Wang
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  3. Jian-qiang Wang
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  5. Lin Li
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Correspondence to Jian-qiang Wang.

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Appendix 1

Appendix 1

See Tables 3 and 4.

Table 3 Comparison of several soft clustering algorithms based on different measurement methods
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Table 4 Comparison of several heuristic optimisation algorithms
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Hou, Wh., Wang, Yt., Wang, Jq. et al. Intuitionistic fuzzy c-means clustering algorithm based on a novel weighted proximity measure and genetic algorithm. Int. J. Mach. Learn. & Cyber. 12, 859–875 (2021). https://doi.org/10.1007/s13042-020-01206-3

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  • DOI: https://doi.org/10.1007/s13042-020-01206-3

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