Genetic-algorithms-based approach to self-organizing feature map and its application in cluster analysis

Mu Chun Su, Hsiao Te Chang

研究成果: 會議貢獻類型會議論文同行評審

4 引文 斯高帕斯(Scopus)

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. Unfortunately, there is no guarantee that the final map will be the most successful (i.e. topologically ordered) map of the whole maps formed during the training procedure. 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 an SOFM formed by the proposed genetic-algorithm-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.

原文???core.languages.en_GB???
頁面735-740
頁數6
出版狀態已出版 - 1998
事件Proceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3) - Anchorage, AK, USA
持續時間: 4 5月 19989 5月 1998

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???event.eventtypes.event.conference???Proceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3)
城市Anchorage, AK, USA
期間4/05/989/05/98

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