教師資料查詢 | 類別: 會議論文 | 教師: 蘇木春 SU MU-CHUN (瀏覽個人網頁)

標題:Genetic-algorithms-based approach to self-organizing feature map and its application in cluster analysis
學年86
學期2
發表日期1998/05/04
作品名稱Genetic-algorithms-based approach to self-organizing feature map and its application in cluster analysis
作品名稱(其他語言)
著者Su, Mu-chun; Chang, Hsiao-te
作品所屬單位淡江大學電機工程學系
出版者Institute of electrical and electronics engineers (IEEE)
會議名稱Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
會議地點Anchorage, AK, USA
摘要In the traditional form of the self-organizing feature map (SOFM) algorithm, the criterion for stopping training is either to terminate the training procedure when no noticeable changes in the feature map are observed or to stop training when the number of iterations reaches a prespecific number. In this paper we propose an efficient method for measuring the degree of topology preservation. Based on the method we apply genetic algorithms (GAs) in two stages to form a topologically ordered feature map. We then use a special method to interpret a SOFM formed by the proposed GA-based method to estimate the number and the locations of clusters from a multidimensional data set without labeling information. Two data sets are used to illustrate the performance of the proposed methods
關鍵字
語言英文
收錄於
會議性質國際
校內研討會地點
研討會時間19980504~19980509
通訊作者蘇木春
國別美國
公開徵稿Y
出版型式
出處Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on (Volume:1 ), pp.735-740
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