Framework of clustering-based outlier detection

6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009

Volumn 1, Issue , 2009, Pages 475-479

  Jiang, Sheng Yi ^a   Yang, Ai Min ^a  

^a GUANGDONG UNIVERSITY OF FOREIGN STUDIES   (China)

Author keywords

[No Author keywords available]

Indexed keywords

DATA SETS; DETECTION APPROACH; DEVIATION DEGREE; OUTLIER DETECTION; THEORETIC ANALYSIS; TIME COMPLEXITY; TWO STAGE;

FUZZY SYSTEMS;

EID: 76349097804     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FSKD.2009.94     Document Type: Conference Paper

References (8)

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3. Knorr E. M., Ng R. T. Algorithms for Mining Distance-Based Outliers in Large Datasets. Proc. 24th Int. Conf. On Very Large Data Bases, New York, NY, 1998:392-403.
4. He Z. , Xu X., Deng S. Discovering cluster-based local outliers Pattern Recognition Letters, 2003,24(9-10):1651-1660.
5. Jiang S., Li Q.,Wang H.,Zhao Y. A Twostage Outlier Detection Approach. Mini-Micro Systems,2005, 26(7):1237-1240.
6. Asuncion, A. & Newman, D.J. UCI Machine Learning Repository [http://www.ics.uci.edu/-mlearn/MLRepository.html]. 2007.
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8. Yamanishi. K, Takeuchi. J,and Williams. G. On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms. In Proc. of the Sixth ACM SIGKDD, Boston, MA, USA, 2000:320-324.