教師資料查詢 | 類別: 期刊論文 | 教師: 余 繁 YU FUN (瀏覽個人網頁)

標題:Adaptive Hyper-Fuzzy Partition Particle Swarm Optimization Clustering Algorithm
學年95
學期1
出版(發表)日期2006/08/01
作品名稱Adaptive Hyper-Fuzzy Partition Particle Swarm Optimization Clustering Algorithm
作品名稱(其他語言)
著者Feng, Hsuan-ming; Chen, Ching-yi; 余繁; Ye, Fun
單位淡江大學電機工程學系
出版者Taylor & Francis
著錄名稱、卷期、頁數Cybernetics and Systems 37(5), pp.463-479
摘要This article presents an adaptive hyper-fuzzy partition particle swarm optimization clustering algorithm to optimally classify different geometrical structure data sets into correct groups. In this architecture, we use a novel hyper-fuzzy partition metric to improve the traditional common-used Euclidean norm metric clustering method. Since one fuzzy rule describes one pattern feature and implies the detection of one cluster center, it is encouraged to decrease the number of fuzzy rules with the hyper-fuzzy partition metric. According to the adaptive particle swarm optimization, it is very suitable to manage the clustering task for a complex, irregular, and high dimensional data set. To demonstrate the robustness of the proposed adaptive hyper-fuzzy partition particle swarm optimization clustering algorithms, various clustering simulations are experimentally compared with K -means and fuzzy c-means learning methods.
關鍵字
語言英文
ISSN0196-9722
期刊性質國內
收錄於
產學合作
通訊作者
審稿制度
國別中華民國
公開徵稿
出版型式,電子版
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