期刊論文
| 學年 | 93 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2005-06-01 |
| 作品名稱 | Alternative KPSO-Clustering Algorithm |
| 作品名稱（其他語言） | |
| 著者 | 余繁; Ye, Fun; Chen, Ching-yi |
| 單位 | 淡江大學電機工程學系 |
| 出版者 | 淡江大學 |
| 著錄名稱、卷期、頁數 | 淡江理工學刊=Tamkang journal of science and engineering 8(2), pp.165-174 |
| 摘要 | This paper presents an evolutionary particle swarm optimization (PSO) learning-based method to optimally cluster N data points into K clusters. The hybrid PSO and K-means algorithm with a novel alternative metric, called Alternative KPSO-clustering (AKPSO), is developed to automatically detect the cluster centers of geometrical structure data sets. The alternative metric is known has more robust ability than the common-used Euclidean norm. In AKPSO algorithm, the special alternative metric is considered to improve the traditional K-means clustering algorithm to deal with various structure data sets. For testing the performance of the proposed method, this paper will show the experience results by using several artificial and real data sets. Simulation results compared with some well-known clustering methods demonstrate the robustness and efficiency of the novel AKPSO method. |
| 關鍵字 | Clustering;Particle Swarm Optimization;K-means |
| 語言 | en_US |
| ISSN | 1560-6686 |
| 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 | ,紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/46130 ) |