Variable precision rough set model
Journal of Computer and System Sciences
Data mining: concepts and techniques
Data mining: concepts and techniques
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Building Data Mining Applications for CRM
Building Data Mining Applications for CRM
Designing Neural Networks using Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
Approaches to knowledge reduction based on variable precision rough set model
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
Research on customer segmentation model by clustering
ICEC '05 Proceedings of the 7th international conference on Electronic commerce
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Mining changing customer segments in dynamic markets
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
RFM Value and Grey Relation Based Customer Segmentation Model in the Logistics Market Segmentation
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 05
Business Aviation Decision-Making Using Rough Sets
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Classifying the segmentation of customer value via RFM model and RS theory
Expert Systems with Applications: An International Journal
A case study of applying data mining techniques in an outfitter's customer value analysis
Expert Systems with Applications: An International Journal
Knowledge discovery on RFM model using Bernoulli sequence
Expert Systems with Applications: An International Journal
Study on Application of Customer Segmentation Based on Data Mining Technology
FCC '09 Proceedings of the 2009 ETP International Conference on Future Computer and Communication
Study on Customer Retention under Dynamic Markets
NSWCTC '10 Proceedings of the 2010 Second International Conference on Networks Security, Wireless Communications and Trusted Computing - Volume 02
Evaluating Learning Algorithms: A Classification Perspective
Evaluating Learning Algorithms: A Classification Perspective
A New Version of the Rule Induction System LERS
Fundamenta Informaticae
| Hi-index | 0.00 |
The customer relationship management (CRM) is a business methodology used to build long term profitable customers by analyzing customer needs and behaviors. The customer behavior is analyzed by choosing important attributes in the customer database. The customers are then segmented into groups according to their attribute values. The rules are generated using rule induction algorithms to describe the customers in each group. These rules can be used by the entrepreneur to predict the behavior of their new customers and to vary the attraction process for existing customers. In this paper a new rule algorithm has been proposed based on the concepts of rough set theory. Its performance has been compared with LEM2 (Learning from Examples Module, version 2) algorithm, an existing rough set based rule induction algorithm. Real data set of the customer transaction is used for analysis. Recency(R), Frequency (F), Monetary (M) and Payment (P) are the attributes chosen for analyzing customer data. The proposed algorithm on average achieves 0.439% increase in sensitivity, 0.007% increase in specificity, 0.151% increase in accuracy, 0.014% increase in positive predictive value, 0.218% increase in negative predictive value and 0.228% increase in F-measure when compared to LEM2 algorithm.