An information entropy-based approach to outlier detection in rough sets

Expert Systems with Applications

Volumn 37, Issue 9, 2010, Pages 6338-6344

  Jiang, Feng ^a   Sui, Yuefei ^b   Cao, Cungen ^b  

^a QINGDAO UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b INSTITUTE OF COMPUTING TECHNOLOGY   (China)

Author keywords

Data mining; Information entropy; Outlier detection; Rough sets

Indexed keywords

DATA HANDLING; INFORMATION THEORY; ROUGH SET THEORY; STATISTICS; UNCERTAINTY ANALYSIS;

COMPETING MECHANISMS; DATA MINING PROBLEMS; EFFECTIVE MEASURES; INFORMATION ENTROPY; OUTLIER DETECTION; SHANNON;

DATA MINING;

EID: 80053607468     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2010.02.087     Document Type: Article

References (34)

1. Aggarwal, C. C., & Yu, P. S. (2001). Outlier detection for high dimensional data. In Proceedings of the 2001 ACM SIGMOD international conference on managment of data (pp. 37-46). California, USA.
2. Barnett, V., & Lewis, T. (1994). Outliers in statistical data. John Wiley and Sons.
3. Bay, S. D. (1999). The UCI KDD repository. Available online at: http://kdd.ics.uci.edu.
4. Beaubouef, T., Petry, F. E., & Arora, G. (1998). Information- theoretic measures of uncertainty for rough sets and rough relational databases. Information Sciences, 109, 535-563.
5. Bolton, R. J., & Hand, D. J. (2002). Statistical fraud detection: A review (with discussion). Statistical Science, 17(3), 235-255.
6. Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000). LOF: identifying densitybased local outliers. In Proceedings of the 2000 ACM SIGMOD international conference on management of data (pp. 93-104). Dallas, USA.
7. Düntsch, I., & Gediga, G. (1998). Uncertainty measures of rough set prediction. Artificial Intelligence, 106, 109-137. (Pubitemid 128402352)
8. Eskin, E., Arnold, A., Prerau, M., Portnoy, L., & Stolfo, S. (2002). A geometric framework for unsupervised anomaly detection: Detecting intrusions in unlabeled data. In D. Barbar et al. (Eds.), Data mining for security applications. Boston: Kluwer Academic Publishers.
9. Harkins, S., He, H. X., Willams, G. J., & Baxter, R. A. (2002). Outlier detection using replicator neural networks. In Proceedings of the fourth international conference on data warehousing and knowledge discovery (pp. 170-180). France.
10. Hawkins, D. (1980). Identifications of outliers. London: Chapman and Hall.
11. He, Z. Y., Deng, S. C., & Xu, X. F. (2005). An optimization model for outlier detection in categorical data. In International conference on intelligent computing (ICIC(1) 2005) (pp. 400-409). Hefei, China.
12. Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: A review. ACM Computing Surveys, 31(3), 264-323.
13. Jiang, F., Sui, Y. F., Cao, & C. G. (2005). Outlier detection using rough set theory. In Proceedings of the 10th international conference on rough sets, fuzzy sets, data mining, and granular computing (RSFDGrC(2) 2005) (pp. 79-87). Regina, Canada: LNAI 3642.
14. Jiang, F., Sui, Y. F., & Cao, C. G. (2006). Outlier detection based on rough membership function. In Proceedings of the fifth international conference on rough set and knowledge technology (RSCTC 2006) (pp. 388-397). Japan: LNAI 4259. (Pubitemid 44860567)
15. Jiang, F., Sui, Y. F., & Cao, C. G. (2009). Some issues about outlier detection in rough set theory. Expert Systems with Applications, 36(3), 4680-4687.
16. Johnson, T., Kwok, I., & Ng, R. T. (1998). Fast computation of 2-dimensional depth contours. In Proceedings of the fourth international conference on knowledge discovery and data mining (pp. 224-228). New York.
17. Knorr, E., & Ng, R. (1998). Algorithms for mining distance-based outliers in large datasets. In Proceedings of the 24th VLDB conference (pp. 392-403). New York.
18. Knorr, E., Ng, R., & Tucakov, V. (2000). Distance-based outliers: Algorithms and applications. VLDB Journal: Very Large Databases, 8(3-4), 237-253.
19. Kovács, L., Vass, D., & Vidács, A. (2004). Improving quality of service parameter prediction with preliminary outlier detection and elimination. In Proceedings of the second international workshop on inter-domain performance and simulation (IPS 2004) (pp. 194-199). Budapest.
20. Lane, T., & Brodley, C. E. (1999). Temporal sequence learning and data reduction for anomaly detection. ACM Transactions on Information and System Security, 2(3), 295-331.
21. Liang, J. Y., & Shi, Z. Z. (2004). The information entropy, rough entropy and knowledge granulation in rough set theory. International Journal of Uncertainty, Fuzziness and Knowledge Based Systems, 12(1), 37-46.
22. Lin, T. Y., & Gereone, N. (1996). Rough sets and data mining: Analysis of imprecise data. Dordrecht: Kluwer Academic.
23. Nguyen, S. H., & Nguyen, H. S. (1996). Some efficient algorithms for rough set methods. In IPMU'96 (pp. 1451-1456). Granada, Spain.
24. Pawlak, Z. (1982). Rough sets. International Journal of Computer and Information Sciences, 11, 341-356.
25. Pawlak, Z. (1991). Rough sets: Theoretical aspects of reasoning about data. Dordrecht: Kluwer Academic Publishers.
26. Pawlak, Z., Grzymala-Busse, J. W., Slowinski, R., & Ziarko, W. (1995). Rough sets. Communications of the ACM, 38(11), 89-95.
27. Ramaswamy, S., Rastogi, R., & Shim, K. (2000). Efficient algorithms for mining outliers from large datasets. In Proceedings of the ACM SIGMOD conference on management of data (pp. 427-438). Dallas.
28. Shannon, C. E. (1948). The mathematical theory of communication. Bell System Technical Journal, 27(3-4), 373-423.
29. Skowron, A., & Rauszer, C. (1992). The discernibility matrices and functions in information systems. Handbook of applications and advances of rough set theory (Vol. 11, pp. 331-362). Dordrecht: Kluwer Academic Publishers.
30. Sui, Y. F., Xia, Y. M., & Wang, J. (2003). The information entropy of rough relational databases. In Proceedings of the ninth international conference on rough sets, fuzzy sets, data mining, and granular computing (pp. 320-324). China. (Pubitemid 36657976)
31. Wierman, M. J. (1999). Measuring uncertainty in rough set theory. International Journal of General Systems, 28(4), 283-297. (Pubitemid 30534574)
32. Willams, G. J., Baxter, R. A., He, H. X., Harkins, S., & Gu, L. F. (2002). A comparative study of RNN for outlier detection in data mining. In Proceedings of the 2002 IEEE international conference on data mining (pp. 709-712). Japan.
33. Yao, Y. Y., Zhao, Y., & Maguire, R. B. (2003). Explanation oriented association mining using rough set theory. In Proceedings of the ninth international conference on rough sets, fuzzy sets, data mining, and granular computing (pp. 165-172). China. (Pubitemid 36657949)
34. Zhong, N., Yao, Y. Y., & Ohshima, M. (2003). Peculiarity oriented multi-database mining. IEEE Transactions on Knowlegde and Data Engineering, 15(4), 952-960.