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A Clustering Algorithm based on Feature Weighting Fuzzy Compactness and Separation

by 1,2,*, 1 and 2
1
College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Nanjing 210016, China
2
College of Electronic and Information Engineering, Nanjing University of Information Science and Technology, 219 Ningliu Road, Nanjing 210044, China
*
Author to whom correspondence should be addressed.
Academic Editor: Toly Chen
Algorithms 2015, 8(2), 128-143; https://doi.org/10.3390/a8020128
Received: 22 January 2015 / Revised: 7 April 2015 / Accepted: 9 April 2015 / Published: 13 April 2015
Aiming at improving the well-known fuzzy compactness and separation algorithm (FCS), this paper proposes a new clustering algorithm based on feature weighting fuzzy compactness and separation (WFCS). In view of the contribution of features to clustering, the proposed algorithm introduces the feature weighting into the objective function. We first formulate the membership and feature weighting, and analyze the membership of data points falling on the crisp boundary, then give the adjustment strategy. The proposed WFCS is validated both on simulated dataset and real dataset. The experimental results demonstrate that the proposed WFCS has the characteristics of hard clustering and fuzzy clustering, and outperforms many existing clustering algorithms with respect to three metrics: Rand Index, Xie-Beni Index and Within-Between(WB) Index. View Full-Text
Keywords: fuzzy clustering; hard clustering; fuzzy compactness and separation; feature weighting fuzzy clustering; hard clustering; fuzzy compactness and separation; feature weighting
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Zhou, Y.; Zuo, H.-F.; Feng, J. A Clustering Algorithm based on Feature Weighting Fuzzy Compactness and Separation. Algorithms 2015, 8, 128-143.

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