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 | 20th International Conference on Database and Expert Systems Applications (DEXA 2009) |
Event Details | 20th International Conference on Database and Expert Systems Applications (DEXA 2009) Parent International Conference on Database and Expert Systems Applications Delivery In person Event Date 31 Aug 2009 to end of 04 Sep 2009 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
Download files
1889
total views787
total downloads3
views this month0
downloads this month