期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2007
卷号：3
期号：3
页码：70-86
出版社：Journal of Theoretical and Applied
摘要：This study is dedicated to proposing a two-stage method, which first uses Self-Organizing Feature Maps (SOM) neural network to determine the number of clusters and cluster centroids, then uses honey bee mating optimization algorithm based on K-means algorithm to find the final solution. The results of simulated data via a Monte Carlo study show that the proposed method outperforms two other methods, SOM followed by K-means (Kuo, Ho & Hu, 2002a) and SOM followed by GAK (Kuo, An, Wang & Chung, 2006), based on both within-cluster variations (SSW) and the number of misclassification. In order to further demonstrate the proposed approach’s capability, a real-world problem of an internet bookstore market segmentation based on customer loyalty is employed. The RFM model is used for comparison of customers' loyalty. Then the proposed method is used to cluster the customers. The results also indicate that the proposed method is better than the other two methods.
