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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
He, Z., Deng, S., & Xu, X. (2005). A unified subspace outlier ensemble framework for outlier detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3739 LNCS, pp. 632–637). https://doi.org/10.1007/11563952_56
Mendeley helps you to discover research relevant for your work.