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Fuzzy C-Means on Metric Lattice

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Abstract

This work proposes a new clustering algorithm named FINFCM by converting original data into fuzzy interval number (FIN) firstly, then it proofs F that denotes the collection of FINs is a lattice and introduce a novel metric distance based on the results from lattice theory as well as combining them with Fuzzy c-means clustering. The relevant mathematical background about lattice theory and the specific procedure which is used to construct FIN have been presented in this paper. Three evaluation indexes including Compactness, RandIndex and Precision are applied to evaluate the performance of FINFCM, FCM and HC algorithm in four experiments used UCI public datasets. The FINFCM algorithm has shown better clustering performance compared to other traditional clustering algorithms and the results are also discussed specifically.

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Funding

This work was supported by the Public Welfare Industry (Agriculture) Research Projects Level-2 under Grant 201503116-04-06; Postdoctoral Foundation of Heilongjiang Province under Grant LBHZ15020; Harbin Applied Technology Research and Development Program under Grant 2017RAQXJ096 and National Key Application Research and Development Program in China under Grant 2018YFD0300105-2.

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Authors and Affiliations

  1. College of Science, Northeast Agricultural University, Harbin, China

    X. Meng, J. Wu, F. Xu & Q. Wu

  2. College of Engineering, Northeast Agricultural University, Harbin, China

    M. Liu

  3. College of Economics and Management, Northeast Agricultural University, Harbin, China

    H. Zhou

Authors
  1. X. Meng
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  2. M. Liu
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  3. H. Zhou
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  4. J. Wu
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  5. F. Xu
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  6. Q. Wu
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Correspondence to X. Meng.

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Meng, X., Liu, M., Zhou, H. et al. Fuzzy C-Means on Metric Lattice. Aut. Control Comp. Sci. 54, 30–38 (2020). https://doi.org/10.3103/S0146411620010071

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