期刊論文
| 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 | en |
| ISSN | 0196-9722 |
| 期刊性質 | 國內 |
| 收錄於 | |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 否 |
| 國別 | TWN |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/46353 ) |