Topology-Preserving Map Formation Achieved with a Genetic Algorithm and Its Application in Cluster Analysis

10.6180/jase.1999.2.1.04

Mu-Chun Su；Hsiao-Te Chang

Cluster Analysis ； Genetic Algorithms ； Neural Networks ； Self-Organizing Feature Maps

淡江理工學刊

2卷1期（1999 / 06 / 01）

29 - 36

英文

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 interprete 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.