A Clustering Algorithm based on Feature Weighting Fuzzy Compactness and Separation

Algorithms

Yuan Zhou²  Hong-fu Zuo²  Jiao Feng¹ 

[1] College of Electronic and Information Engineering, Nanjing University of Information Science and Technology, 219 Ningliu Road, Nanjing 210044, China; E-Mail:;College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Nanjing 210016, China; E-Mail:

关键词: fuzzy clustering;    hard clustering;    fuzzy compactness and separation;    feature weighting;   

DOI  :  10.3390/a8020128

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.