Zhang, Ji ORCID: https://orcid.org/0000-0001-7167-6970 and Gao, Qigang and Wang, Hai and Liu, Qing and Xu, Kai
(2009)
Detecting projected outliers in high-dimensional data streams.
In: DEXA 2009: 20th International Conference on Database and Expert Systems Applications, 31 Aug- 4Sep 2009, Linz, Austria.
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Abstract
In this paper, we study the problem of projected outlier detection in high dimensional data streams and propose a new technique, called Stream Projected Ouliter deTector (SPOT), to identify outliers embedded in subspaces. Sparse Subspace Template (SST), a set of subspaces obtained by unsupervised and/or supervised learning processes, is constructed in SPOT to detect projected outliers effectively. Multi-Objective Genetic Algorithm (MOGA) is employed as an effective search method for finding outlying subspaces from training data to construct SST. SST is able to carry out online self-evolution in the detection stage to cope with dynamics of data streams. The experimental results demonstrate the efficiency and effectiveness of SPOT in detecting outliers in high-dimensional data streams.
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