Discovering cluster-based local outliers

Pattern Recognition Letters

Volumn 24, Issue 9-10, 2003, Pages 1641-1650

  He, Zengyou ^a   Xu, Xiaofei ^a   Deng, Shengchun ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Clustering; Data mining; Outlier detection

Indexed keywords

ALGORITHMS; DATA REDUCTION;

CLUSTER-BASED LOCAL OUTLIERS;

PATTERN RECOGNITION;

NUMERICAL METHOD;

EID: 0037410488     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-8655(03)00003-5     Document Type: Article

References (21)

1. Aggarwal, C., Yu, P., 2001. Outlier detection for high dimensional data. In: Proceedings of SIGMOD'01, Santa Barbara, CA, USA, pp. 37-46.
2. Angiulli, F., Pizzuti, C., 2002. Fast outlier detection in high dimensional spaces. In: Proceedings of PKDD'02.
3. Arning, A., Agrawal, R., Raghavan, P., 1996. A linear method for deviation detection in large databases. In: Proceedings of KDD'96, Portland OR, USA, pp. 164-169.
4. Barnett, V., Lewis, T., 1994. Outliers in Statistical Data. John Wiley and Sons, New York, p. 1994.
5. Beyer, K., Goldstein, J., Ramakrishnan, R., Shaft, U., 1999. When is "nearest neighbors" meaningful? In: Proceedings of ICDT'99, Jerusalem, Israel, pp. 217-235.
6. Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J., 2000. LOF: Identifying density-based local outliers. In: Proceedings of SIGMOD'00, Dallas, Texas, pp. 427-438.
7. Ester, M., Kriegel, H.P., Sander, J., Xu, X., 1996. A density-based algorithm for discovering clusters in large spatial databases. In: Proceedings of KDD'96, Portland OR, USA, pp. 226-231
8. Guha, S., Rastogi, R., Kyuseok, S., 1999. ROCK: A robust clustering algorithm for categorical attributes. In: Proceedings of ICDE'99, Sydney, Australia, pp. 512-521.
9. Harkins, S., He, H., Willams, G.J., Baster, R.A., 2002. Outlier detection using replicator neural networks. In: Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery, Aix-en-Provence, France, pp. 170-180.
10. He, Z., Deng, S., Xu, X., 2002a. Outlier detection integrating semantic knowledge. In: Proceedings of the 3rd International Conference on Web-Age Information Management, Beijing, China, pp.126-131
11. He, Z., Xu, X., Deng, S., 2002b. Squeezer: An efficient algorithm for clustering categorical data. J. Comput. Sci. Technol. 17 (5), 611-624.
12. Jiang, M.F., Tseng, S.S., Su, C.M., 2001. Two-phase clustering process for outliers detection. Pattern Recognition Lett. 22 (6/7), 691-700.
13. Knorr, E.M., Ng, R.T., 1998. Algorithms for mining distance-based outliers in large datasets. In: Proceedings of VLDB'98, New York, USA, pp. 392-403
14. Liu, B., Hsu, W., Ma, Y., 1998. Integrating classification and association rule mining. In: Proceedings of KDD'98, New York, USA, pp. 80-86.
15. Merz, C.J., Merphy, P., 1996. UCI repository of machinelearning databases. URL: http://www.ics.uci.edu/mlearn/MLRRepository.html.
16. Nanopoulos, A., Theodoridis, Y., Manolopoulos, Y., 2001. C2P: Clustering based on closest pairs. In: Proceedings of VLDB'01, Rome Italy, pp. 331-340.
17. Nuts, R., Rousseeuw, P. 1996. Computing depth contours of bivariate point clouds. J. Comput. Statist. Data Anal. 23, 153-168.
18. Ramaswamy, S., Rastogi, R., Kyuseok, S., 2000. Efficient algorithms for mining outliers from large data sets. In: Proceedings of SIGMOD'00, Dallas, Texas, pp. 93-104
19. Yamanishi, K., Takeuchi, J., 2001. Discovering outlier filtering rules from unlabeled data-combining a supervised learner with an unsupervised learner. In: Proceedings of KDD'01 pp. 389-394
20. Yamanishi, K., Takeuchi, J., Williams, G., 2000. On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms. In: Proceedings of KDD'00, Boston, MA, USA, pp. 320-325.
21. Yu, D., Sheikholeslami, G., Zhang, A., 1999. Find Out: Finding out outliers in large datasets. Technique Report, State University of New York at Buffalo, 1999