Finding centric local outliers in categorical/numerical spaces

Knowledge and Information Systems

Volumn 9, Issue 3, 2006, Pages 309-338

  Yu, Jeffrey Xu ^a   Qian, Weining ^b   Lu, Hongjun ^c   Zhou, Aoying ^b  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^b FUDAN UNIVERSITY   (China)

^c HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

Clustering; Data mining; Outlier detection

Indexed keywords

ANOMALY DETECTION; CRIME; DATA HANDLING; DATA MINING; INVESTMENTS; REINFORCEMENT;

BANK INVESTMENTS; CATEGORICAL DATA; CLUSTERING; CREDIT CARD FRAUD DETECTIONS; CRIMINAL ACTIVITIES; LOCAL OUTLIERS; MUTUAL REINFORCEMENT; REAL DATA SETS;

STATISTICS;

EID: 33645552151     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-005-0197-6     Document Type: Article

References (23)

1. Aggarwal C, Yu P (2001) Outlier detection for high dimensional data. In: Proceedings of ACM SIGMOD international conference on management of data. ACM, New York, pp 37-47
2. Barnett V, Lewis T (1994) Outliers in statistical data. Wiley, New York
3. Breunig M, Kriegel H-P, Ng R, Sander J (1999) Optics-of: Identifying local outliers. In: Proccedings of the 3rd European conference on principles and practice of knowledge discovery in databases. Springer, Berlin Heidelberg New York, pp 262-270
4. Breunig M, Kriegel H-P, Ng R, Sander J (2000) Lof: Identifying density-based local outliers. In: Proceedings of the ACM SIGMOD international conference on management of data. ACM, New York, pp 93-104
5. Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd International conference on knowledge discovery and data mining. AAAI, Manlo Park, CA, pp 226-231
6. Guha S, Rastogi R, Shim K (1998) Cure: An efficient clustering algorithm for large databases. In: Proceedings of the ACM SIGMOD international conference on management of data. ACM, New York, pp 73-84
7. Guha S, Rastogi R, Shim K (1999) Rock: A robust clustering algorithm for categorical attributes. In: Proceedings of the IEEE international conference on data engineering. IEEE Computer Society, Morristown, NJ
8. Hawkins D (1980) Identification of outliers. Chapman and Hall, London
9. Hinneburg A, Keim D (1998) An efficient approach to clustering in large multimedia databases with noise. In: Proceedings of the 4th international conference on knowledge discovery and data mining. AAAI, Menlo Park, CA, pp 58-65
10. Jin W, Tung AK, Han J (2001) Mining top-n local outliers in large databases. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 293-298
11. Karypis G, Han E, Kumar V (1999) Chameleon: A hierarchical clustering algorithm using dynamic modeling. IEEE Computing 32 (8):68-75
12. Kleinberg J (1998) Authoritative sources in a hyperlinked environment In: Proceedings of the 9th ACM-SIAM symposium on discrete algorithms
13. Knorr E, Ng R (1998) Algorithms for mining distance-based outliers in large datasets. In: Proceedings of the 24th international conference on very large data bases. Morgan Kaufmann, San Mateo, CA, pp 392-403
14. Knorr E, Ng R (1999) Finding intensional knowledge of distance-based outliers. In: Proceedings of the 25th international conference on very large data bases. Morgan Kaufmann, San Mateo, CA, pp 211-222
15. Ng R, Han J (1994) Efficient and effective clustering methods for spatial data mining. In: Proceedings of the 20th international conference on very large data bases. Morgan Kaufmann, San Mateo, CA, pp 144-155
16. Preparata F, Shamos M (1988) Computational geometry: An introduction. Springer, Berlin Heidelberg New York
17. Ramaswamy S, Rastogi R, Shim K (2000) Efficient algorithms for mining outliers from large data sets. In: Proceedings of the ACM SIGMOD international conference on management of data. ACM, New York, pp 427-438
18. Ruts I, Rousseeuw P (1996) Computing depth contours of bivariate point clouds. J Comput Stat Data Anal 23: 153-168
19. Sheikholeslami G, Chatterjee S, Zhang A (1998) Wavecluster: A multi-resolution clustering approach for very large spatial databases. In: Proceedings of 24th international conference on very large data bases. Morgan Kaufmann, San Mateo, Ca, pp 428-439
20. Shekhar S, Lu C-T, Zhang P (2001) Detecting graph-based spatial outliers: Algorithms and applications (a summary of results). In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York
21. Tang J, Chen Z, Fu A W-C, Cheung D (2001) A robust outlier detection scheme for large data sets. Technical report. http://www.cs.panam.edu/ chen/paper-file/outlierpaper.ps
22. Wang W, Yang J, Muntz R (1997) Sting: A statistical information grid approach to spatial data mining. In: Proceedings of the 23rd international conference on very large data bases. Morgan Kaufmann, San Mateo, CA, pp 186-195
23. Zhang T, Ramakrishnan R, Linvy M (1996) Birch: An efficient data clustering method for very large databases. In: Proceedings of the ACM SIGMOD international conference on management of data. ACM, New York, pp 103-114