A consistency-based validation for data clustering

Subtitle:

Article type: Research Article

Authors: Zhu, Bing^{a; *} | He, Changzheng^a | Jiang, Xiaoyi^b

Affiliations: [a] Business School, Sichuan University, Chengdu, Sichuan, China | [b] Department of Computer Science, University of Muenster, Muenster, Germany

Correspondence: [*] Corresponding author: Bing Zhu, Business School, Sichuan University, Chengdu 610064, Sichuan, China. Tel.: +86 28 8540 3495; Fax: +86 28 8541 8891; E-mail:[email protected]

Abstract: Clustering analysis is a powerful tool in customer segmentation. Although various algorithms have been proposed, the determination of the optimal number of clusters remains to be a difficult issue. In this paper, a clustering method based on consistency criterion is proposed to address this issue. The main characteristic of the new approach is that it requires little prior information and can find the optimal number of clusters automatically. Extensive comparisons are done over 22 real-world datasets from different domains, in which four well-known clustering algorithms in combination with six clustering indices are used as the benchmark methods. The results demonstrate the superiority of our method in appropriately determining the number of clusters. An application of the new approach in customer segmentation of credit card users is also illustrated.

Keywords: Customer segmentation, cluster analysis, number of clusters, consistency criterion

DOI: 10.3233/IDA-150727

Journal: Intelligent Data Analysis, vol. 19, no. 3, pp. 471-484, 2015