Autonomous rule induction from data with tolerances in customer relationship management

Authors:
Tzu-Liang (Bill) Tseng;Chun-Che Huang;Yu-Neng Fan
Affiliations:
Department of Mechanical and Industrial Engineering, The University of Texas at E1 Paso, 500 West University Avenue, El Paso, TX 79968, USA;Department of Information Management, National Chi Nan University, No. 1, University Road, Puli, Nantou 545, Taiwan, ROC;Department of Information Management, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 5
Cited 1

Concurrent Engineering: Automation, Tools, and Techniques

Concurrent Engineering: Automation, Tools, and Techniques
Using Rough Sets to Build-up Web-Based One to One Customer Services

COMPSAC '00 24th International Computer Software and Applications Conference
Quantitative approaches for information modeling

Quantitative approaches for information modeling
Feature-based recommendations for one-to-one marketing

Expert Systems with Applications: An International Journal
Information gain and divergence-based feature selection for machine learning-based text categorization

Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval

Alternative rule induction methods based on incremental object using rough set theory

Applied Soft Computing

Quantified Score

Hi-index	12.05

Visualization

Abstract

In this paper, application of the rough set theory (RST) to feature selection in customer relationship management (CRM) is introduced. Compared to other methods, the RST approach has the advantage of combining both qualitative and quantitative information in the decision analysis, which is extremely important for CRM. Automated decision support for CRM has been proposed in recent years. However, little work has been devoted to the development of computer-based systems to support CRM in rule induction. This paper presents a novel rough set based algorithm for automated decision support for CRM. Particularly, the approach is capable to handle real numbers instead of integer numbers through introduction of converted numbers involving tolerances. Being unique and useful in solving CRM problems, an alternative rule extraction algorithm (AREA) is presented for discovering preference-based rules according to the reducts which contain the maximum of strength index (SI) in the same case, where the data with tolerance. The empirical data set associated with CRM has proven the validity and reliability of these approaches. This research thus contributes to developing and validating a useful approach to automated decision support for CRM. This paper forms the basis for solving many other similar problems that occur in the service industry.