Zhang, Ji 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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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.
|Item Type:||Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)|
|Additional Information:||Author's version deposited in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.com)|
|Uncontrolled Keywords:||stream projected outlier deTector; SPOT; outlier detection; atmospheric temperature; clustering algorithms; data communication systems; database systems; detectors|
|Subjects:||280000 Information, Computing and Communication Sciences|
|Depositing User:||Dr Ji Zhang|
|Date Deposited:||09 Sep 2009 23:53|
|Last Modified:||02 Jul 2013 23:23|
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