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Odabk: An Effective Approach to Detecting Outlier in Data Stream

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3 Author(s)
Feng Han ; Sch. of Comput. Sci. & Technol., North China Electr. Power Univ., Baoding ; Yan-Ming Wang ; Hua-Peng Wang

Currently, data mining in data stream becomes a very popular research field. One of the central tasks in mining data streams is that of identifying outliers which can lead to discovering unexpected and interesting knowledge, which is critical important. To effectively mine outliers in data stream, ODABK, an algorithm for outlier detection in data stream is presented. It is based on KNN and significantly enhanced by means of other data structures and its optimized logical operations. Finally, the paper reports experiments on a real-world census data which show that ODABK is more effective in detection rate and execution times

Published in:

Machine Learning and Cybernetics, 2006 International Conference on

Date of Conference:

13-16 Aug. 2006