Data mining: concepts and techniques
Data mining: concepts and techniques
Mining the Web: Transforming Customer Data into Customer Value
Mining the Web: Transforming Customer Data into Customer Value
Optimal Database Marketing: Strategy, Development, and Data Mining
Optimal Database Marketing: Strategy, Development, and Data Mining
Applications of Data Mining to Electronic Commerce
Data Mining and Knowledge Discovery
Computers and Industrial Engineering
Combination of multiple classifiers for the customer's purchase behavior prediction
Decision Support Systems - Special issue: Agents and e-commerce business models
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
Strategic Database Marketing
International Journal of Electronic Commerce
Outlier identification and market segmentation using kernel-based clustering techniques
Expert Systems with Applications: An International Journal
Mining important association rules based on the RFMD technique
International Journal of Data Analysis Techniques and Strategies
Management and forecast of dynamic customer needs: An artificial immune and neural system approach
Advanced Engineering Informatics
Apply robust segmentation to the service industry using kernel induced fuzzy clustering techniques
Expert Systems with Applications: An International Journal
Discovering and usage of customer knowledge in QoS mechanism for B2C web server systems
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part II
Review: Soft computing applications in customer segmentation: State-of-art review and critique
Expert Systems with Applications: An International Journal
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Using the customer relationship management perspective to investigate customer behavior, this study differentiates between customers through customer segmentation, tracks customer shifts from segment to segment over time, discovers customer segment knowledge to build an individual transition path and a dominant transition path, and then predicts customer segment behavior patterns. By using real-world data, this study evaluates the accuracy of predictive models. The concluding remarks discuss future research in this area.