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

Mu Chun Su, Hsiao Te Chang

Research output: Contribution to conferencePaperpeer-review

4 Scopus citations

Abstract

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.

Original languageEnglish
Pages735-740
Number of pages6
StatePublished - 1998
EventProceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3) - Anchorage, AK, USA
Duration: 4 May 19989 May 1998

Conference

ConferenceProceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3)
CityAnchorage, AK, USA
Period4/05/989/05/98

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