Referenced Items are not available for this document.
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
- Page(s):
- 1036 - 1041
- Print ISBN:
- 1-4244-0061-9
- INSPEC Accession Number:
- 9143890
- Conference Location :
- Dalian, China
- Digital Object Identifier :
- 10.1109/ICMLC.2006.258556
- Product Type:
- Conference Publications
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