Skip to Main content Skip to Navigation
Conference papers

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.
Document type :
Conference papers
Complete list of metadatas

Cited literature [10 references]  Display  Hide  Download

https://hal.inria.fr/hal-02197804
Contributor : Hal Ifip <>
Submitted on : Tuesday, July 30, 2019 - 5:02:29 PM
Last modification on : Wednesday, February 5, 2020 - 5:54:01 PM

File

 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2021-01-01

Please log in to resquest access to the document

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

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⟩

Share

Metrics

Record views

113