A Customer Segmentation Model Based on Affinity Propagation Algorithm and Improved Genetic K-Means Algorithm - Intelligent Information Processing IX
Conference Papers Year : 2018

A Customer Segmentation Model Based on Affinity Propagation Algorithm and Improved Genetic K-Means Algorithm

Abstract

Customer Relationship Management System (CRM) has accumulated massive customer transaction data. Effective customer segmentation by analyzing transaction data can contribute to marketing strategy designing. However, the state-of-the-art researches are defective such as the uncertain number of clusters and the low accuracy. In this paper, a novel customer segmentation model, AP-GKAs, is proposed. First, factor analysis extracts customer feature based on multi-indicator RFM model. Then, affinity propagation (AP) determines the number of customer clusters. Finally, the improved genetic K-means algorithm (GKAs) is used to increase clustering accuracy. The experimental results showed that the AP-GKAs has higher segmentation performance in comparison to other typical methods.
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hal-02197804 , version 1 (30-07-2019)

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Meiyang Zhang, Zili Zhang, Shi Qiu. A Customer Segmentation Model Based on Affinity Propagation Algorithm and Improved Genetic K-Means Algorithm. 10th International Conference on Intelligent Information Processing (IIP), Oct 2018, Nanning, China. pp.321-327, ⟨10.1007/978-3-030-00828-4_32⟩. ⟨hal-02197804⟩
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