Zhang, Ji and Lou, Meng and Ling, Tok Wang and Wang, Hai (2004) HOS-Miner: a system for detecting outlying subspaces of high-dimensional data. In: 30th International Conference on Very Large Data Bases (VLDB'04), 31 Aug - 3 Sept 2004, Toronto, Canada.
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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.
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| Item Type: | Conference or Workshop Item (Commonwealth Reporting Category E) (Paper) |
|---|---|
| Refereed: | Yes |
| Item Status: | Live Archive |
| Additional Information: | Author's version deposited in accordance with the copyright policy of the publisher. |
| Faculty/School / Institute/Centre: | Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013) |
| Faculty/School / Institute/Centre: | Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013) |
| Date Deposited: | 28 Sep 2009 06:24 |
| Last Modified: | 02 Jul 2013 23:23 |
| Uncontrolled Keywords: | HOS-Miner; High-dimensional Outlying Subspaces |
| Fields of Research (2008): | 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining |
| URI: | http://eprints.usq.edu.au/id/eprint/5654 |
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