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.
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He, Z., Deng, S., Xu, X. (2005). A Unified Subspace Outlier Ensemble Framework for Outlier Detection. In: Fan, W., Wu, Z., Yang, J. (eds) Advances in Web-Age Information Management. WAIM 2005. Lecture Notes in Computer Science, vol 3739. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11563952_56
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DOI: https://doi.org/10.1007/11563952_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29227-2
Online ISBN: 978-3-540-32087-6
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