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 | 
|---|---|
| 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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