A review for mobile commerce research and applications
Decision Support Systems
A recommender system using GA K-means clustering in an online shopping market
Expert Systems with Applications: An International Journal
An intelligent market segmentation system using k-means and particle swarm optimization
Expert Systems with Applications: An International Journal
Fast modified global k-means algorithm for incremental cluster construction
Pattern Recognition
Particle swarm optimization with selective particle regeneration for data clustering
Expert Systems with Applications: An International Journal
A global best artificial bee colony algorithm for global optimization
Journal of Computational and Applied Mathematics
A modified Artificial Bee Colony algorithm for real-parameter optimization
Information Sciences: an International Journal
Customer data mining for lifestyle segmentation
Expert Systems with Applications: An International Journal
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Customer segmentation can enable company administrators to establish good customer relations and refine their marketing strategies to match customer expectations. To achieve optimal segmentation, a hybrid Artificial Bee Colony algorithm ABC is proposed to classify customers in mobile e-commerce environment, which is named KP-ABC. KP-ABC is based on three famous algorithms: the K-means, Particle Swarm Optimization PSO, and ABC. The author first applied five clustering algorithms to a mobile customer segmentation problem using data collected from a well established chain restaurant which has operations throughout Japan. The results from the clustering were compared to the existing company customer segmentation data for verifications. Based on the initial analysis, special characteristics from those three algorithms were extracted and modified in our KP-ABC method which performed extremely well with mobile e-commerce applications. The result shows that KP-ABC is at least 2% higher than that of other three algorithms.