A proposed outliers identification algorithm for categorical data sets

INFOS2010 - 2010 7th International Conference on Informatics and Systems

Volumn , Issue , 2010, Pages

  Taha, Ayman ^a   Hegazy, Osman M ^a  

^a Cairo University   (Egypt)

Author keywords

Categorical data; Data mining; Outliers detection

Indexed keywords

CATEGORICAL DATA; CATEGORICAL VARIABLES; IDENTIFICATION ALGORITHMS; IDENTIFICATION METHOD; MEASURING DISTANCES; OUTLIERS DETECTION;

ALGORITHMS; DATA MINING;

INFORMATION SCIENCE;

EID: 77953171946     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. P. Angiulli and S. Basta. Distance-based detection and prediction of outliers. IEEE Transactions on Knowledge and Data Engineering, 18:145-160, 2006.
2. S. Bay and M. Schwabacher. Mining distance-based outliers in near linear time with randomization and a simple pruning rule. In Proceedings of the 9th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 29-38, 2003.
3. N. Billor, A. Hadi, and P. Velleman. Blocked adaptive computationally efficient outlier nominators. Computational Statistics and Data Analysis, 34:279-298, 2000.
4. S. Boriah, V. Chandola, and V. Kumar. Similarity measures for categorical data: A comparative evaluation. In In Proceedings of 2008 SIAM Data Mining Conference, pages 243-254, Atlanta, GA., April 2008.
5. V. Chandola, S. Boriah, and V. Kumar. A framework for exploring categorical data. In In Proceedings of 2009 SIAM Data Mining Conference, Sparks, NV, 1-13 October 2009.
6. K. Das, J. Schneider, and D. B. Neill. Anomaly pattern detection in categorical datasets. In KDD'08, pages 169-176, 2008.
7. D.M. Hawkins. Identification of Outliers. Chapman and Hall, 1980.
8. Z. He, X. Xu, and S. Deng. An optimization model for outlier detection in categorical data. In International Conference on Intelligent Computing, pages 400-409, 2005.
9. Z. He, X. Xu, and S. Deng. A fast greedy algorithm for outlier mining. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 567-576, Seoul-Korea, 2006.
10. Z. He, X. Xu, J. Z. Huang, and S. Deng. Fp-outlier: Frequent pattern based outlier detection. Computer Science and Information Systems (COMSIS), 2:726-732, 2005.
11. V.J Hodge and J. Austin. A survey of outlier detection methodologies. Artificial Intelligence Review, 22:85-126, 2004.
12. S. Y. Jiang, X. Song, H. Wang, J. J. Han, and Q. H. Li. A clustering-based method for unsupervised intrusion detections. Pattern Recognition Letters, 27:802-810, 2006.
13. A. Koufakou, M. Georgiopoulos, and G. Anagnostopoulos. Detecting outliers in high-dimensional datasets with mixed attributes. In International Conference on Data Mining (DMIN 2008), L.Vegas,NV, 14-17 2008.
14. A. Koufakou, E. Ortiz, M. Georgiopoulos, G. Anagnostopoulos, and K. Reynolds. A scalable and efficient outlier detection strategy for categorical data. In IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Patras-Peloponnese-Greece, 29-31 October 2007.
15. S. Lin and D. E. Brown. An outlier-based data association method for linking criminal incidents. Decision Support Systems, 41: 604âǍŞ-615, March 2006.
16. K. Narita and H. Kitagawa. Detecting outliers in categorical record databases based on attribute associations. Progress in WWW Research and Development, 4976:111-123, 2008.
17. M. E. Otey, A. Ghoting, and S. Parthasarathy. Fast distributed outlier detection in mixed-attribute data sets. Data Mining and Knowledge Discovery, 12(2-3):203-228, May 2006.
18. S. Shekhar, C. T. Lu, and Pusheng Zhang. Detecting graph-based spatial outliers: algorithms and applications. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pages 371-376, San Francisco, California, 2001.
19. M. Shyu, K. Sarinnapakorn, I. Kuruppu-Appuhamilage, S. Chen, L. W. Chang, and T. Goldring. Handling nominal features in anomaly intrusion detection problems. In International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications, pages 55-62, 2005.