ACM Computing Surveys (CSUR)
Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Novelty detection: a review—part 2: neural network based approaches
Signal Processing
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
A kernel-based subtractive clustering method
Pattern Recognition Letters
Market segmentation based on hierarchical self-organizing map for markets of multimedia on demand
Expert Systems with Applications: An International Journal
Applying knowledge engineering techniques to customer analysis in the service industry
Advanced Engineering Informatics
Segmentation of stock trading customers according to potential value
Expert Systems with Applications: An International Journal
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
An introduction to kernel-based learning algorithms
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
Apply robust segmentation to the service industry using kernel induced fuzzy clustering techniques
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Robust data clustering by learning multi-metric Lq-norm distances
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Review: Soft computing applications in customer segmentation: State-of-art review and critique
Expert Systems with Applications: An International Journal
Category role aided market segmentation approach to convenience store chain category management
Decision Support Systems
Review: A review of novelty detection
Signal Processing
| Hi-index | 12.06 |
Customer relationship management (CRM) has become a major business strategy of the leading millennium since it can help decision makers to understand customers' profiles more clearly. For a successful CRM, it is important for a company to target the most profitable customers and to manage them through providing a variety of attractive and personalized goods or service. With proper market segmentation, companies can deploy the right resource to target groups and develop closer relationships with them more efficiently and effectively. Recently, there are many ways proposed by CRM researchers or marketers for effective market segmentation. Most of them, however, are not robust to outliers and/or cannot work well when the target clusters are overlapped. To solve this challenging problem, this paper using kernel-based clustering techniques presents a hybrid approach for outlier identification and robust segmentation in real application. Two real datasets, including the Iris and the automobile maintenance, are used to validate the proposed approach. Experimental results show that the proposed approach cannot only identify outliers in advance, but also achieve better segmentation.