ACM Computing Surveys (CSUR)
Integration of self-organizing feature map and K-means algorithm for market segmentation
Computers and Operations Research
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Novelty detection: a review—part 2: neural network based approaches
Signal Processing
Fuzzy discriminant analysis with outlier detection by genetic algorithm
Computers and Operations Research
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Application of SVM and ANN for intrusion detection
Computers and Operations Research
Computer assisted customer churn management: State-of-the-art and future trends
Computers and Operations Research
Applying knowledge engineering techniques to customer analysis in the service industry
Advanced Engineering Informatics
Outlier identification and market segmentation using kernel-based clustering techniques
Expert Systems with Applications: An International Journal
Segmentation of stock trading customers according to potential value
Expert Systems with Applications: An International Journal
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
Robust fuzzy clustering of relational data
IEEE Transactions on Fuzzy Systems
Neural fraud detection in credit card operations
IEEE Transactions on Neural Networks
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Using the Taguchi method for effective market segmentation
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
To understand customers' characteristics and their desire is critical for modern CRM (customer relationship management). The easiest way for a company to achieve this goal is to target their customers and then to serve them through providing a variety of personalized and satisfactory goods or service. In order to put the right products or services and allocate resources to specific targeted groups, many CRM researchers and/or practitioners attempt to provide a variety of ways for effective customer segmentation. Unfortunately, most existing approaches are vulnerable to outliers in practice and hence segmentation results may be unsatisfactory or seriously biased. In this study, a hybrid approach that incorporates kernel induced fuzzy clustering techniques is proposed to overcome the above-mentioned difficulties. Two real datasets, including the WINE and the RFM, are used to validate the proposed approach. Experimental results show that the proposed approach cannot only fulfill robust classification, but also achieve robust segmentation when applied to the noisy dataset.