Outlier identification and market segmentation using kernel-based clustering techniques

Expert Systems with Applications

Volumn 36, Issue 2 PART 2, 2009, Pages 3744-3750

  Wang, Chih Hsuan ^a  

^a MING CHUAN UNIVERSITY   (Taiwan)

Author keywords

Kernel based clustering; Market segmentation; Outlier identification

Indexed keywords

CLUSTER ANALYSIS; DECISION MAKING; PUBLIC RELATIONS; SALES; STATISTICS;

BUSINESS STRATEGY; CUSTOMER RELATIONSHIP MANAGEMENT; KERNEL-BASED CLUSTERING; MARKET SEGMENTATION; OUTLIER IDENTIFICATION; PROFITABLE CUSTOMER; REAL APPLICATIONS; ROBUST SEGMENTATION;

COMMERCE;

EID: 56349122418     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2008.02.037     Document Type: Article

References (30)

1. Aggarwal, C. C., & Yu, P. S. (2001). Outlier detection for high dimensional data. In Proceedings of the ACM SIGMOD conference.
2. Barnett V., and Lewis T. Outliers in statistical data (1994), John Wiley & Sons, New York
3. Bezdek J.C. Pattern recognition with fuzzy objective function algorithms (1981), Plenum Press, New York
4. Chen C.H., Khoo C.L., and Yan W. A strategy for acquiring customer requirement patterns using laddering technique and ART2 neural network. Advanced Engineering Informatics 16 (2002) 229-240
5. Davé R.N., and Krishnapuram R. Robust clustering models: A unified view. IEEE Transactions on Fuzzy Systems 5 2 (1997) 270-293
6. Davé R.N., and Sen S. Robust fuzzy clustering of relational data. IEEE Transactions on Fuzzy Systems 10 6 (2002) 713-726
7. Girolami M. Mercer kernel based clustering in the feature space. IEEE Transactions on Neural Networks 13 3 (2002) 780-784
8. Ha S.H. Applying knowledge engineering techniques to customer analysis in the service industry. Advanced Engineering Informatics 21 (2007) 293-301
9. Ha S.H., and Park S.C. Application of data mining tools to hotel data mart on the Intranet for database marketing. Expert Systems with Applications 15 (1998) 1-31
10. He Z., Xu X., Huang J.Z., and Deng S. Mining class outliers: Concepts, algorithms and applications in CRM. Expert Systems with Applications 27 (2004) 681-697
11. Hodge V.J., and Austin J. Survey of outlier detection methodologies. Artificial Intelligence Review 22 (2004) 85-126
12. Hruschka H., and Natter M. Comparing performance of feedforward neural nets and K-means for cluster based market segmentation. European Journal of Operational Research 114 (1999) 346-353
13. Huang J.H., Tzeng G.H., and Ong C.S. Marketing segmentation using support vector clustering. Expert Systems with Applications 32 (2007) 313-317
14. Hung C., and Tsai C.F. Market segmentation on hierarchical self-organizing map for markets of multimedia on demand. Expert Systems with Applications 34 (2008) 780-787
15. Hwang H., Jung T., and Suh E. An LTV model and customer segmentation based on customer value: A case study on the wireless telecommunication industry. Expert Systems with Applications 26 (2004) 181-188
16. Jain A.K., Murty M.N., and Flynn P.J. Data clustering: A review. ACM Computing Survey 3 3 (1999) 264-323
17. Kim D.W., Lee K.Y., Lee D., and Lee K.H. Evaluation of the performance of clustering algorithms in kernel-induced feature space. Pattern Recognition 38 (2005) 607-611
18. Kim D.W., Lee K.Y., Lee D., and Lee K.H. A kernel-based subtractive clustering method. Pattern Recognition Letters 26 (2005) 879-891
19. Krishnapuram R., and Keller J.M. A possibilistic approach to clustering. IEEE Transactions on Fuzzy systems 1 1 (1993) 98-110
20. Kuo R.J., An Y.L., Wang H.S., and Chung W.J. Integration of self-organizing feature maps neural network and genetic K-means algorithm for market segmentation. Expert Systems with Applications 30 (2006) 213-324
21. Lee S.C., Suh Y.H., Kim J.K., and Lee K.J. A cross-national market segmentation of online game industry using SOM. Expert Systems with Applications 27 (2004) 559-570
22. Markou M., and Singh S. Novelty detection: A review. Part 1: Statistical approaches. Signal Processing 83 12 (2003) 2481-2497
23. Markou M., and Singh S. Novelty detection: A review. Part 2: Neural network based approaches. Signal Processing 83 12 (2003) 2499-2521
24. McCarty J.A., and Hastak M. Segmentation approaches in data-mining: A comparison of RFM, CHAID and logistic regression. Journal of Business Research 20 (2007) 656-662
25. Müller K.R., Mika S., Rätsoh G., Tusda K., and Schölkopf B. An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks 12 2 (2001) 181-201
26. Ozer M. User segmentation of online music services using fuzzy clustering. Omega 29 (2001) 193-206
27. Shin H.W., and Sohn S.Y. Segmentation of stock trading customers according to potential value. Expert Systems with Applications 27 (2004) 27-33
28. Tsai C.Y., and Chiu C.C. A purchase-based market segmentation methodology. Expert Systems with Applications 27 (2004) 265-276
29. Vellido A., Lisboa P.J.G., and Meehan K. Segmentation of the on-line shopping market using neural networks. Expert Systems with Applications 17 (1999) 303-314
30. Xu R., and Wunsch D. Survey of clustering algorithms. IEEE Transactions on Neural Networks 16 3 (2005) 645-678