Introduction to the theory of neural computation
Introduction to the theory of neural computation
Future Generation Computer Systems - Special double issue on data mining
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Toward Global Optimization of Case-Based Reasoning Systems for Financial Forecasting
Applied Intelligence
Segmentation of stock trading customers according to potential value
Expert Systems with Applications: An International Journal
Handling sequential pattern decay: Developing a two-stage collaborative recommender system
Electronic Commerce Research and Applications
Fuzzy time series prediction using hierarchical clustering algorithms
Expert Systems with Applications: An International Journal
Knowledge-Based Systems
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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.