聚类分析
模糊聚类
模式识别(心理学)
k-中位数聚类
欧几里德距离
火焰团簇
相关聚类
人工智能
树冠聚类算法
CURE数据聚类算法
数学
单连锁聚类
模糊逻辑
计算机科学
数据挖掘
作者
Xingchen Zhu,Xiaohong Wu,Bin Wu,Haoxiang Zhou
出处
期刊:Journal of Intelligent and Fuzzy Systems
[IOS Press]
日期:2023-06-01
卷期号:44 (6): 9847-9862
被引量:2
摘要
The fuzzy c-mean (FCM) clustering algorithm is a typical algorithm using Euclidean distance for data clustering and it is also one of the most popular fuzzy clustering algorithms. However, FCM does not perform well in noisy environments due to its possible constraints. To improve the clustering accuracy of item varieties, an improved fuzzy c-mean (IFCM) clustering algorithm is proposed in this paper. IFCM uses the Euclidean distance function as a new distance measure which can give small weights to noisy data and large weights to compact data. FCM, possibilistic C-means (PCM) clustering, possibilistic fuzzy C-means (PFCM) clustering and IFCM are run to compare their clustering effects on several data samples. The clustering accuracies of IFCM in five datasets IRIS, IRIS3D, IRIS2D, Wine, Meat and Apple achieve 92.7%, 92.0%, 90.7%, 81.5%, 94.2% and 88.0% respectively, which are the highest among the four algorithms. The final simulation results show that IFCM has better robustness, higher clustering accuracy and better clustering centers, and it can successfully cluster item varieties.
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