Abstract
This study proposes novel clustering algorithm based on genetic algorithms (GAs) to carry out a segmentation of the online shopping market effectively. In general, GAs are believed to be effective on NP-complete global optimization problems and they can provide good sub-optimal solutions in reasonable time. Thus, we believe that a clustering technique with GA can provide a way of finding the relevant clusters. This paper applies GA-based K-means clustering to the real-world online shopping market segmentation case for personalized recommender systems. In this study, we compare the results of GA-based K-means to those of traditional K-means algorithm and self-organizing maps. The result shows that GA-based K-means clustering may improve segmentation performance in comparison to other typical clustering algorithms.
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