Customer segmentation analysis based on SOM clustering

Author

Li, Ying ; Lin, Feng

Author_Institution

Bus. Sch., East China Univ. of Sci. & Technol., Shanghai

Volume

1

fYear

2008

fDate

12-15 Oct. 2008

Firstpage

15

Lastpage

19

Abstract

From the angle of customer value and customer behavior, this paper utilizes data mining methods to segment the clients in security industry. Clustering algorithm is a kind of customer segmentation methods commonly used in data mining. In this article, a two-stage integration of K-means clustering algorithm and SOM network is applied to segment customers and finally forms groups of clients with different features. Through analyzing different groups of customers, we try to position the target clients of the company properly.

Keywords

consumer behaviour; customer profiles; data mining; security; self-organising feature maps; statistical analysis; K-means clustering algorithm; SOM clustering; customer behavior; customer segmentation analysis; customer value; data mining methods; security industry; Algorithm design and analysis; Clustering algorithms; Data mining; Data security; Databases; Economic forecasting; Mining industry; Performance analysis; Telegraphy; Telephony; Customer behavior analysis; Customer value analysis; K-means clustering; SOM network;

fLanguage

English

Publisher

ieee

Conference_Titel

Service Operations and Logistics, and Informatics, 2008. IEEE/SOLI 2008. IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-2012-4

Electronic_ISBN

978-1-4244-2013-1

Type

conf

DOI

10.1109/SOLI.2008.4686353

Filename

4686353