A Weighting Fuzzy Clustering Algorithm Based on Euclidean Distance

被引：5
|
作者：
Xue, Zhan-ao [1 ]
Cen, Feng [1 ]
Wei, Li-ping [1 ]
机构：
[1] Henan Normal Univ, Coll Comp & Informat Technol, Xinxiang 453007, Peoples R China
来源：
FIFTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 1, PROCEEDINGS | 2008年
关键词：
D O I：
10.1109/FSKD.2008.98
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Considering a user's actual demand, this paper analyzed the functional requirments which can be brought forward by a user of a clustering system and proposed a fuzzy c-means clustering algorithm based on Euclidean distance. In this algorithm, weights are directly appointed by a user or a domanial expert. Different weights show the distinction of the user's recognition of different character criterion. Compared with the traditional Fuzzy c-means clustering method, this algorithm can improve the clustering's flexibility and produce a more satisfactory clustering result.
引用
收藏
页码：172 / 175
页数：4
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