HOS-Miner: a system for detecting outlying subspaces of high-dimensional data
Paper
Paper/Presentation Title | HOS-Miner: a system for detecting outlying subspaces of high-dimensional data |
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Presentation Type | Paper |
Authors | Zhang, Ji (Author), Lou, Meng (Author), Ling, Tok Wang (Author) and Wang, Hai (Author) |
Editors | Nascimento, Mario A., Özsu, M. Tamer, Kossmann, Donald, Miller, Renee J., Blakeley, Jose A. and Schiefer, K. Bernhard |
Journal or Proceedings Title | Proceedings of the 30th International Conference on Very Large Data Bases (VLDB'04) |
Number of Pages | 4 |
Year | 2004 |
Place of Publication | San Francisco, California, United States |
ISBN | 0120884690 |
Web Address (URL) of Paper | http://www.cs.toronto.edu/vldb04/ |
Conference/Event | 30th International Conference on Very Large Data Bases (VLDB'04) |
Event Details | 30th International Conference on Very Large Data Bases (VLDB'04) Event Date 31 Aug 2004 to end of 03 Sep 2004 Event Location Toronto, Canada |
Abstract | [Abstract]: We identify a new and interesting high-dimensional outlier detection problem in this paper that is, detecting the subspaces in which given data points are outliers. We call the subspaces in which a data point is an outlier as its Outlying Subspaces. In this paper, we will propose the prototype of a dynamic subspace search system, called HOS-Miner (HOS stands for High-dimensional Outlying Subspaces) that utilizes a sample-based learning process to effectively identify the outlying subspaces of a given point. |
Keywords | HOS-Miner; High-dimensional Outlying Subspaces |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | University of Toronto, Canada |
National University of Singapore |
https://research.usq.edu.au/item/9z2qq/hos-miner-a-system-for-detecting-outlying-subspaces-of-high-dimensional-data
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