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Fuzzy rough clustering for categorical data

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Abstract

Unlabeled categorical data is common in many applications. Because there is no geometric structure for categorical data, how to discover knowledge and patterns from unlabeled categorical data is an important problem. In this paper, a fuzzy rough clustering algorithm for categorical data is proposed. The proposed algorithm uses the partition of each attribute to calculate the granularity of each attribute and introduces information granularity to measure the significance of each attribute. It is different from traditional clustering algorithms for categorical data that the proposed algorithm can transform categorical data set into numeric data set and introduces a nonlinear dimension reduction algorithm to decrease the dimensions of data set. The proposed algorithm and the comparison algorithms are executed on real data sets. The experimental results show that the proposed algorithm outperforms the comparison algorithms on the most data sets and the results prove that the proposed algorithm is an effective clustering algorithm for categorical data sets.

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Acknowledgements

This work was supported by National Key Research and Development Program of China (Nos.2017YFB1300200, 2017YFB1300203), National Natural Science Fund of China (Nos.61972064, 61672130, 61602082, 61627808, 91648205), the Open Program of State Key Laboratory of Software Architecture (No.SKLSAOP1701), LiaoNing Revitalization Talents Program (No. XLYC1806006), the Fundamental Research Funds for the Central Universities (Nos. DUT19RC(3)012, DUT17RC(3)071) and the development of science and technology of Guangdong province special fund project (No.2016B090910001). The authors are grateful to the editor and the anonymous reviewers for constructive comments that helped to improve the quality and presentation of this paper.

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Authors and Affiliations

  1. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China

    Shuliang Xu & Jian Zhou

  2. School of Innovation and Entrepreneurship, Dalian University of Technology, Dalian, China

    Shenglan Liu & Lin Feng

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  1. Shuliang Xu
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  2. Shenglan Liu
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  3. Jian Zhou
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  4. Lin Feng
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Correspondence to Lin Feng.

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Xu, S., Liu, S., Zhou, J. et al. Fuzzy rough clustering for categorical data. Int. J. Mach. Learn. & Cyber. 10, 3213–3223 (2019). https://doi.org/10.1007/s13042-019-01012-6

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