聚类分析
初始化
加权
分歧(语言学)
计算机科学
模糊聚类
数据挖掘
星团(航天器)
k-中位数聚类
特征(语言学)
模糊逻辑
模式识别(心理学)
人工智能
CURE数据聚类算法
医学
放射科
哲学
语言学
程序设计语言
作者
Ziheng Wu,Yuan Zhao,Wenyan Wang,Cong Li
出处
期刊:Neurocomputing
[Elsevier BV]
日期:2023-07-14
卷期号:552: 126550-126550
被引量:6
标识
DOI:10.1016/j.neucom.2023.126550
摘要
Fuzzy C-means clustering (FCM) approach is an effective method for clustering and has been successfully applied in numbers of real-world problems. In this paper, we propose an improving adaptive weighted FCM based on data divergence, with the merits of three aspects: 1) to avoid randomization of cluster centers, we propose a new cluster centers initialization method; 2) we present an adaptive parameter reflecting the changes of intra-cluster data divergence in the process of cluster formation from iteration to iteration for correcting the unreasonable factors resulting from the changes timely; 3) we propose a new data weighting method. By integrating the adaptive parameter and feature weighting method, we propose a novel adaptive objective function, by which the updating iterative formulas of the membership degrees, the feature weights and the cluster centers are obtained. Experimental results have shown that the novel clustering approach put forward can improve the clustering performance effectively.
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