Outlier detection integrating semantic knowledge

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

Volumn 2419, Issue , 2002, Pages 123-131

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

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ANOMALY DETECTION; DATA HANDLING; INFORMATION MANAGEMENT; SEMANTICS;

DATA POINTS; SEMANTIC KNOWLEDGE; SEMANTIC OUTLIERS;

STATISTICS;

EID: 84944057338     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45703-8_12     Document Type: Conference Paper

References (6)

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2. S. Ramaswamy, R. Rastogi, S. Kyuseok: Efficient Algorithms for Mining Outliers from Large Data Sets. Proc ACM SIGMOD 2000 Int Conf on Management of Data, Dallas, Texas, 2000.
3. M. M. Breunig, H. P. Kriegel, R. T. Ng, J. Sander: LOF: Identifying Density-Based Local Outliers”. Proc ACM SIGMOD 2000 Int Conf on Management of Data, Dallas, Texas, 2000.
4. C. Aggarwal, P. Yu: Outlier Detection for High Dimensional Data. Proc. of the 2001 ACM SIGMOD Int’1 Conf. Management of Data, pp. 37-46, Santa Barbara, CA, USA.
5. Z. He, S. Deng and X. Xu: Squeezer: An Efficient Algorithm for Clustering Categorical Data. Technical Report, HIT, 2001. http://202.118.239.67/tech/squeezer.pdf. To appear in Journal of Computer Science and Technology.
6. C. J. Merz, Murphy: UCI Repository of Machine Learning Databases. (Http://www.ics.uci.edu/~mlearn/MLRRepository.html).