Detecting projected outliers in high-dimensional data streams
Paper
Paper/Presentation Title | Detecting projected outliers in high-dimensional data streams |
---|---|
Presentation Type | Paper |
Authors | Zhang, Ji (Author), Gao, Qigang (Author), Wang, Hai (Author), Liu, Qing (Author) and Xu, Kai (Author) |
Editors | Bhowmick, Sourav S., Kung, Josef and Wagner, Roland |
Journal or Proceedings Title | Lecture Notes in Computer Science (Book series) |
Journal Citation | 5690 |
Number of Pages | 16 |
Year | 2009 |
Publisher | Springer |
Place of Publication | Germany |
ISSN | 1611-3349 |
0302-9743 | |
ISBN | 9783642035722 |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-642-03573-9_53 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-3-642-03573-9_53 |
Conference/Event | DEXA 2009: 20th International Conference on Database and Expert Systems Applications |
Event Details | DEXA 2009: 20th International Conference on Database and Expert Systems Applications Event Location Linz, Austria |
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. |
Keywords | stream projected outlier deTector; SPOT; outlier detection; atmospheric temperature; clustering algorithms; data communication systems; database systems; detectors |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
460599. Data management and data science not elsewhere classified | |
461399. Theory of computation not elsewhere classified | |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia |
Dalhousie University, Canada | |
Saint Mary's University, Canada |
https://research.usq.edu.au/item/9z26v/detecting-projected-outliers-in-high-dimensional-data-streams
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