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Soft subspace clustering with an improved feature weight self-adjustment mechanism

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Abstract

Traditional clustering algorithms are often defeated by high dimensionality. In order to find clusters hiding in different subspaces, soft subspace clustering has become an effective means of dealing with high dimensional data. However, most existing soft subspace clustering algorithms contain parameters which are difficult to be determined by users in real-world applications. A new soft subspace clustering algorithm named SC-IFWSA is proposed, which uses an improved feature weight self-adjustment mechanism IFWSA to update adaptively the weights of all features for each cluster according to the importance of the features to clustering quality and does not require users to set any parameter values. In addition, SC-IFWSA can overcome the traditional FWSA mechanism which may fail to calculate feature weights in some particular cases. In comparison with its related approaches, the experimental results carried out on ten data sets demonstrate the effectiveness and feasibility of the proposed method.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant No. 61070062, the Key Project on the Cooperation of Industry and University of Fujian Province of China under Grant No. 2010H6007, and the Key Scientific Research Project of the Higher Education Institutions of Fujian Province of China under Grant No. JK2009006.

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Authors and Affiliations

  1. School of Mathematics and Computer Science, Fujian Normal University, Fuzhou, China

    Gongde Guo, Si Chen & Lifei Chen

  2. Key Laboratory of Network Security and Cryptography, Fujian Normal University, Fuzhou, China

    Gongde Guo, Si Chen & Lifei Chen

Authors
  1. Gongde Guo
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  2. Si Chen
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  3. Lifei Chen
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Correspondence to Gongde Guo.

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Guo, G., Chen, S. & Chen, L. Soft subspace clustering with an improved feature weight self-adjustment mechanism. Int. J. Mach. Learn. & Cyber. 3, 39–49 (2012). https://doi.org/10.1007/s13042-011-0038-8

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  • DOI: https://doi.org/10.1007/s13042-011-0038-8

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