A unified subspace outlier ensemble framework for outlier detection

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3739 LNCS, Issue , 2005, Pages 632-637

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

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; DATABASE SYSTEMS; INFORMATION MANAGEMENT; WORLD WIDE WEB;

CATEGORICAL DATASETS; ENSEMBLE-LEARNING; OUTLIER DETECTION;

LEARNING SYSTEMS;

EID: 33646512600     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11563952_56     Document Type: Conference Paper

References (7)

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2. He, Z., Xu, X., Deng, S.: Discovering Cluster Based Local Outliers. Pattern Recognition Letters, 2003, 24(9-10): 1641-1650
3. Ramaswamy, S., Rastogi, R., Kyuseok, S.: Efficient Algorithms for Mining Outliers from Large Data Sets. In: Proc. of SIGMOD'00, pp. 93-104, 2000
4. Aggarwal, C., Yu, P.: Outlier Detection for High Dimensional Data. In: Proc. of SIGMOD'01, pp. 37-46, 2001
5. Merz, G., Murphy, P.: Uci Repository of Machine Learning Databases, http://www.ics.uci.edu/mlearn/MLRepository.html, 1996
6. Harkins, S., et al.: Outlier Detection Using Replicator Neural Networks. In: Proc. of DaWaK'02, pp. 170-180, 2002
7. He, Z., Xu, X., Huang, J., Deng, S.: Mining Class Outliers: Concepts, Algorithms and Applications in CRM. Expert System With Applications, 2004, 27(4): 681-697