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

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...

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Bibliographic Details
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
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Summary: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.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2016.04.017