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The Global k-Means Clustering Analysis Based on Multi-Granulations Nearness Neighborhood

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Abstract

Multi-Granulations nearness approximation space is a new generalized model of approximation spaces, in which topology neighborhoods are induced by multi probe functions with many category features. In this paper, by combining global k-means clustering algorithms and topology neighborhoods, two k-means clustering algorithms are proposed, in which AFS topology neighborhoods are employed to determine the clustering initial points. The proposed method can be applied to the data sets with numerical, Boolean, linguistic rating scale, sub-preference relations features. The illustrative examples show that the proposed method is effective for clustering problems, and can enrich the applicable field on the idea of qualitatively near.

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Authors and Affiliations

  1. Department of Mathematics, Dalian Maritime University, Dalian, 116026, P.R. China

    Lidong Wang, Xiaodong Liu & Yashuang Mu

Authors
  1. Lidong Wang
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  2. Xiaodong Liu
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  3. Yashuang Mu
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Correspondence to Lidong Wang.

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Wang, L., Liu, X. & Mu, Y. The Global k-Means Clustering Analysis Based on Multi-Granulations Nearness Neighborhood. Math.Comput.Sci. 7, 113–124 (2013). https://doi.org/10.1007/s11786-013-0150-0

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  • DOI: https://doi.org/10.1007/s11786-013-0150-0

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