A unified subspace outlier ensemble framework for outlier detection

Authors:
Zengyou He;Shengchun Deng;Xiaofei Xu
Affiliations:
Department of Computer Science and Engineering, Harbin Institute of Technology, China;Department of Computer Science and Engineering, Harbin Institute of Technology, China;Department of Computer Science and Engineering, Harbin Institute of Technology, China
Venue:
WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management
Year:
2005

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Abstract

This paper proposes a unified framework for outlier detection in high dimensional spaces from an ensemble-learning viewpoint. Moreover, to demonstrate the usefulness of our framework, we developed a very simple and fast algorithm, namely SOE1, in which only subspaces with one dimension is used for mining outliers from large categorical datasets. Experimental results demonstrate the superiority of SOE1 algorithm.