Odabk: An Effective Approach to Detecting Outlier in Data Stream

Author

Han, Feng ; Wang, Yan-ming ; Wang, Hua-peng

Author_Institution

Sch. of Comput. Sci. & Technol., North China Electr. Power Univ., Baoding

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

1036

Lastpage

1041

Abstract

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

Keywords

data mining; data structures; pattern classification; ODABK algorithm; data mining; data stream; data structure; knowledge discovery; outlier detection; pattern classification; Algorithm design and analysis; Computer science; Credit cards; Cybernetics; Data mining; Data structures; Databases; Detection algorithms; Distributed computing; Electronic commerce; Electronic mail; Machine learning; Weather forecasting; KNN-based; Outlier detection; data stream; neighborhood;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.258556

Filename

4028216