Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (9): 156-161.DOI: 10.3778/j.issn.1002-8331.1901-0117

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Improved Fuzzy C-Means Clustering Validity Index

YAN Jiazhan, CHEN Hua, LI Yang   

  1. 1.College of Electrical Engineering, Xinjiang University, Urumqi 830047, China
    2.College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, China
  • Online:2020-05-01 Published:2020-04-29



  1. 1.新疆大学 电气工程学院,乌鲁木齐 830047
    2.山东科技大学 计算机科学与工程学院,山东 青岛 266590


Aiming at the problem that the existing evaluation index of fuzzy C-means does not involve the real geometric distribution structure and prior information of data sets, an improved evaluation index VCSC is proposed to accurately find clusters that match the natural distribution of data samples. This index defines the compactness measure by combining the sum of squared errors, membership degree weights and root number weights of cluster data, the minimum distance between cluster centers, membership degree and the distance between cluster centers and average cluster centers and the definition separation degree, and the combination measure is defined by combining the range of membership degree and the distribution of samples. The experimental results show that the proposed index can effectively evaluate the clustering results and accurately get the best number of clusters in the data.

Key words: fuzzy clustering, validity index, membership degree, geometric structure, optimal clustering



关键词: 模糊聚类, 有效性指标, 隶属度, 几何结构, 最佳聚类