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Spectral co-clustering documents and words using fuzzy K-harmonic means

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Abstract

This paper analyzes the main steps of spectral co-clustering documents and words, finds out its cause of sensitivity to input order, and presents a modified method of spectral co-clustering documents and words based on fuzzy K-harmonic means. This method consists of two steps. The first step constructs Laplacian matrix which is insensitive to input order. The second step exploits fuzzy K-harmonic means algorithm instead of K-means algorithm to obtain clustering results. Fuzzy K-harmonic means algorithm uses fuzzy weight distance while calculating the distance between each data points and cluster centers. The experiments show that the proposed method not only is insensitive to input order, but also can improve the accuracy and robustness of clustering results.

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Acknowledgment

The author would like to express thanks to the anonymous reviewers for their insightful comments that helped improve this paper. This work is supported by National Natural Science Funds (No. 61175053,61073133), Innovative Team and Key Scientific Research Projects of Ministry of Education (No. 2011ZD010).

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Authors and Affiliations

  1. Department of Information Science and Technology, Dalian Maritime University, Dalian, China

    Na Liu, Fei Chen & Mingyu Lu

  2. Department of Information Science and Engineering, Dalian Polytechnic University, Dalian, China

    Na Liu

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  1. Na Liu
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  2. Fei Chen
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  3. Mingyu Lu
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Correspondence to Mingyu Lu.

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Liu, N., Chen, F. & Lu, M. Spectral co-clustering documents and words using fuzzy K-harmonic means. Int. J. Mach. Learn. & Cyber. 4, 75–83 (2013). https://doi.org/10.1007/s13042-012-0077-9

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  • DOI: https://doi.org/10.1007/s13042-012-0077-9

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