A New Robust Classification Method for CRM

Author

Xiaoyu, Li ; Changzheng, He ; Liatsis, Panos

Author_Institution

Bus. Sch., Sichuan Uni., Chengdu, China

fYear

2010

fDate

17-18 April 2010

Firstpage

70

Lastpage

73

Abstract

Customer classification is a key step in customer relationship management (CRM), and there are many methods used for it, such as Neural Net, association rules, SOM model, etc. However, most existing methods don´t take noise which is very common in reality into consideration. In this paper, we combine Croup Method of Data Handling (GMDH) with Takagi and Sugeno fuzzy model (TS) to form a new classification method TS-GMDH. The experimental result shows that TS-GMDH outperforms the benchmark classifiers when the noise level is high.

Keywords

customer relationship management; data handling; fuzzy control; pattern classification; CRM; SOM model; Takagi-Sugeno fuzzy model; association rules; benchmark classifiers; customer relationship management; group method of data handling; neural net; robust customer classification method; Association rules; Customer relationship management; Data mining; Engineering management; Fuzzy sets; Helium; Mathematical model; Neural networks; Robustness; Wearable computers; TS-GMDH; customer; experiment; noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Wearable Computing Systems (APWCS), 2010 Asia-Pacific Conference on

Conference_Location

Shenzhen

Print_ISBN

978-1-4244-6467-8

Electronic_ISBN

978-1-4244-6468-5

Type

conf

DOI

10.1109/APWCS.2010.25

Filename

5481089