Mining class outliers: Concepts, algorithms and applications in CRM

Expert Systems with Applications

Volumn 27, Issue 4, 2004, Pages 681-697

  He, Zengyou ^a   Xu, Xiaofei ^a   Huang, Joshua Zhexue ^b   Deng, Shengchun ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

CRM; Data mining; Direct marketing; Outlier

Indexed keywords

ALGORITHMS; CUSTOMER SATISFACTION; DATABASE SYSTEMS; MARKETING; NEURAL NETWORKS; SECURITY OF DATA; SEMANTICS;

CRM; DATASETS; DIRECT MARKETING; OUTLIER;

DATA MINING;

EID: 4544379047     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1010/j.eswa.2004.07.002     Document Type: Review

References (52)

1. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules Proceedings of the VLDB94 pp. 478-499.
2. Aggarwal, C., & Yu, P. (2001). Outlier detection for high dimensional data Proceedings of the SIGMOD01 pp. 37-46.
3. Angiulli, F., & Pizzuti, C. (2002). Fast outlier detection in high dimensional spaces Proceedings of the PKDD02 pp. 25-36.
4. Arndt D, Gersten W. (2001). Data management in analytical customer relationship management. Data Mining for Marketing Applications Workshop at ECML/PKDD.
5. Arning, A., Agrawal, R., & Raghavan, P. (1996). A linear method for deviation detection in large databases Proceedings of the KDD96 pp. 164-169.
6. Barnett, V., & Lewis, T. (1994). Outliers in statistical data. New York: Wiley.
7. Bay, S. D., & Schwabacher, M. (2003). Mining distance based outliers in near linear time with randomization and a simple pruning rule Proceedings of the KDD03.
8. Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, J. (2000). LOF: identifying density-based local outliers Proceedings of the SICMOD00 pp. 93-104.
9. Cao, L. J., Lee, H. P., & Chong, W. K. (2003). Modified support vector novelty detector using training data with outliers. Pattern Recognition Letters, 24(14), 2479-2487.
10. Chiu, A. L., & Fu, A. W. (2003). Enhancements on local outlier detection Proceedings of the IDEAS03.
11. ECCS, (1999). CRM Defining customer relationship marketing and management, http://www.eccs.uk.com/crm/crmdefinitions/define.asp.
12. Elkan, C. (2001). Magical thinking in data mining: lessons from CoIL challenge 2000 Proceedings of the KDD01.
13. Gibson, D., Kleiberg, J., & Raghavan, P. (1998). Clustering categorical data: an approach based on dynamic systems Proceedings of the VLDB98 pp. 311-323.
14. Han, J., Pei, J., & Yin, J. (2000). Mining frequent patterns without candidate generation Proceedings of the SICMOD00 pp. 1-12.
15. Harkins, S., He, H., Willams, G. J., & Baster, R. A. (2002). Outlier detection using replicator neural networks Proceedings of the DaWaK02 pp. 170-180.
16. Hawkins, D. (1980). Identification of outliers. Reading, London: Chapman & Hall.
17. He, Z., Deng, S., & Xu, X. (2002). Outlier detection integrating semantic knowledge Proceedings of the WAIM02 pp. 126-131.
18. He, Z., Xu, X., & Deng, S. (2003). Discovering cluster based local outliers. Pattern Recognition Letters, 24(9-10), 1651-1660.
19. He, Z., Xu, X., & Deng, S. (2002a). Squeezer: an efficient algorithm for clustering categorical data. Journal of Computer Science and Technology, 17(5), 611-624.
20. He, Z., Xu, X., & Deng, S. (2002b). FP-outlier: Frequent pattern based outlier detection Technology Report. Harbin Institute of Technology.
21. Hu, T., & Sung, S. Y. (2003). Detecting pattern-based outliers. Pattern Recognition Letters, 24(16), 3059-3068.
22. Jiang, M. F., Tseng, S. S., & Su, C. M. (2001). Two-phase clustering process for outliers detection. Pattern Recognition Letters, 22(6,7), 691-700.
23. Jin, W., Tung, A. K. H., & Han, J. (2001). Mining top-n local outliers in large databases Proceedings of the KDD01 pp. 293-298.
24. Johnson, T., Kwok, I., & Ng, R. (1998). Fast computation of 2-dimensional depth contours Proceedings of the KDD98 pp. 224-228.
25. Knorr, E., & Ng, R. (1997). A unified notion of outliers: Properties and computation Proceedings of the KDD97 pp. 219-222.
26. Knorr, E., & Ng, R. (1998). Algorithms for mining distance-based outliers in large datasets Proceedings of the VLDB98 pp. 392-403.
27. Knorr, E., & Ng, R. (1999). Finding intentional knowledge of distance-based outliers Proceedings of the VLDB99 pp. 211-222.
28. Knorr, E., Ng, R., & Tucakov, T. (2000). Distance-based outliers: Algorithms and applications. VLDB Journal, 8(3 and 4), 237-253.
29. Lewandowski, A. (2000). How to detect potential customers CoIL Challenge 2000: The Insurance Company Case, Technical Report 2000-09. Netherlands: Leiden Institute of Advanced Computer Science.
30. Ling, C. X., & Li, C. (1998). Data mining for direct marketing: Problems and solutions Proceedings of the KDD98 pp. 73-79.
31. Liu, B., Ma, Y., Wong, C. K., & Yu, P. S. (2003). Scoring the data using association rules. Applied Intelligence, 18(2), 119-135.
32. Merz, G., & Murphy, P. (1996). Uci repository of machine learning databases Technical Report. University of California: Department of Information and Computer Science http://www.ics.uci.edu/mlearn/MLRepository.html.
33. Nuts, R., & Rousseeuw, P. (1996). Computing depth contours of bivariate point clouds. Computational Statistics and Data Analysis, 23, 153-168.
34. Papadimitriou, S., & Faloutsos, C. (2003). Cross-outlier detection Proceedings of the of SSTD03 pp. 199-213.
35. Papadimitriou, S., Kitagawa, H., Gibbons, P. B., & Faloutsos, C. (2003). Fast outlier detection using the local correlation integal Proceedings of the ICDE03.
36. Petrovskiy, M. (2003). A hybrid method for patterns mining and outliers detection in the web usage log Proceedings of the AWIC03 pp. 318-328.
37. Ramaswamy, S., Rastogi, R., & Kyuseok, S. (2000). Efficient algorithms for mining outliers from large data sets Proceedings of the SIGMOD00 pp. 93-104.
38. Rousseeuw, P., & Leroy, A. (1987). Robust regression and outlier detection. New York: Wiley.
39. Schölkopf, B., Platt, J., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high dimensional distribution. Neural Computation, 13(7), 1443-1472.
40. Setnes, M., & Kaymak, U. (2001). Fuzzy modeling of client preference from large data sets: an application to target selection in direct marketing. IEEE Transactions on Fuzzy Systems, 9(1), 153-163.
41. Skinner S. J., (1990). Marketing Houghton Mifflin, Boston et al.
42. SPSS Inc (1993). SPSS CHAID for Windows 6.0. Englewood Cliffs, NJ: Prentice-Hall.
43. Tang, T., Chen, Z., Fu, A. W., & Cheung, D. W. (2002). Enhancing effectiveness of outlier detections for low density patterns Proceedings of the PAKDD02 pp. 535-548.
44. Tax, D. M. J., & Duin, R. P. W. (1999). Support vector data description. Pattern Recognition Letters, 20(11-13), 1191-1199.
45. The Coil dataset can found at: http://www.liacs.nl/(putten/library/ cc2000/.
46. Wei, L., Qian, W., Zhou, A., Jin, W., & Yu, J. X. (2003). HOT: hypergraph-based outlier test for categorical data Proceedings of the PAKDD03 pp. 399-410.
47. Willams, G. J., Baster, R. A., He, H., Harkins, S., & Gu, L. (2002). A comparative study of rnn for outlier detection in data mining Proceedings of the ICDM02 pp. 709-712.
48. Yamanishi, K., & Takeuchi, J. (2001). Discovering outlier filtering rules from unlabeled data-combining a supervised learner with an unsupervised learner Proceedings of the KDD01 pp. 389-394.
49. Yamanishi, K., Takeuchi, J., & Williams, G. (2000). On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms Proceedings of the KDD00 pp. 320-325.
50. Yao, Y., Zhong, N., Huang, J., Ou, C., & Liu, C. (2002). Using market value functions for targeted marketing data mining. International Journal of Pattern Recognition and Artificial Intelligence, 16(8), 1117-1132.
51. Yu, D., Sheikholeslami, G., & Zhang, A. (2002). Findoul: finding out outliers in large datasets. Knowledge and Information Systems, 4(4), 387-412.
52. Zaki, M. J. (2000). Scalable algorithms for association mining. IEEE Transactions On Knowledge and Data Engineering, 12(3), 372-390.