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A customer segmentation model based on affinity propagation algorithm and improved genetic k-means algorithm

conference contribution
posted on 2018-01-01, 00:00 authored by M Zhang, Zili ZhangZili Zhang, S Qiu
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.

History

Volume

538

Pagination

321-327

Location

Nanning, China

Start date

2018-10-19

End date

2018-10-22

ISSN

1868-4238

ISBN-13

9783030008277

Language

eng

Publication classification

E Conference publication, E1 Full written paper - refereed

Editor/Contributor(s)

Shi Z, Mercier-Laurent E, Li J

Title of proceedings

IIP 2018 : Proceedings of 10th IFIP TC 12 International Conference on Intelligent Information Processing

Event

Intelligent Information Processing IX. Conference (10th : 2018 : Nanning, China)

Publisher

Springer

Place of publication

Cham, Switzerland

Series

IFIP Advances in Information and Communication Technology