An application of a metaheuristic algorithm-based clustering ensemble method to APP customer segmentation

• Published in: Neurocomputing (Amsterdam) 2016-09, Vol.205, p.116-129
• Main Authors: Kuo, R.J., Mei, C.H., Zulvia, F.E., Tsai, C.Y.
• Format: Article
• Language: English
• Subjects: Algorithms; Applications programs; Artificial bee colony; Cluster analysis; Clustering; Clustering ensembles; Customer segmentation; Genetic algorithm; Mobile communication systems; Optimization; Particle swarm optimization; Segmentation; Strategy; Swarm intelligence
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2016.04.017

This study proposes a metaheuristic-based clustering ensemble method. It integrates the clustering ensembles algorithm with the metaheuristic-based clustering algorithm. In the clustering ensembles, this study performs an improved generation mechanism and a co-association matrix in the co-occurrence approach. In order to improve the efficiency, a principle component analysis is employed. Furthermore, three metaheuristic-based clustering algorithms are proposed. This paper uses a real-coded genetic algorithm, a particle swarm optimization and an artificial bee colony optimization to combine with clustering ensembles algorithm. The experimental results indicate that the proposed metaheuristic-based clustering ensembles algorithms have better performance than metaheuristic-based clustering without clustering ensembles method. Furthermore, the proposed algorithms are applied to solve a customer segmentation problem. The real problem is come from a mobile application. Among all of the proposed algorithms, the artificial bee colony optimization-based clustering ensembles algorithm outperforms other algorithms. Therefore, the marketing strategy for the real application is made based on the best result.