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Attribute weighted mercer kernel based fuzzy clustering algorithm for general non-spherical datasets

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Abstract

Clustering analysis is an important topic in artificial intelligence, data mining and pattern recognition research. Conventional clustering algorithms, for instance, the famous Fuzzy C-means clustering algorithm (FCM), assume that all the attributes are equally relevant to all the clusters. However in most domains, especially for high-dimensional dataset, some attributes are irrelevant, and some relevant ones are less important than others with respect to a specific class. In this paper, such imbalances between the attributes are considered and a new weighted fuzzy kernel-clustering algorithm (WFKCA) is presented. WFKCA performs clustering in a kernel feature space mapped by mercer kernels. Compared with the conventional hard kernel-clustering algorithm, WFKCA can yield the meaningful prototypes (cluster centers) of the clusters. Numerical convergence properties of WFKCA are also discussed. For in-depth studies, WFKCA is extended to WFKCA2, which has been demonstrated as a useful tool for clustering incomplete data. Numerical examples demonstrate the effectiveness of the new WFKCA algorithm

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Authors and Affiliations

  1. Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, Shanghai, P.R. China, 200030

    Hongbin Shen, Jie Yang & Xiaojun Liu

  2. Department of Computer Science, Southern Yangtze University, Wuxi, Jiangsu, P.R. China, 214046

    Shitong Wang

Authors
  1. Hongbin Shen
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  2. Jie Yang
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  3. Shitong Wang
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  4. Xiaojun Liu
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Correspondence to Hongbin Shen.

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Shen, H., Yang, J., Wang, S. et al. Attribute weighted mercer kernel based fuzzy clustering algorithm for general non-spherical datasets. Soft Comput 10, 1061–1073 (2006). https://doi.org/10.1007/s00500-005-0043-5

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  • DOI: https://doi.org/10.1007/s00500-005-0043-5

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