HOS-Miner: a system for detecting outlying subspaces of high-dimensional data

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
Fields of Research (2020):	46 INFORMATION AND COMPUTING SCIENCES > 4699 Other information and computing sciences > 469999 Other information and computing sciences not elsewhere classified
URI:	http://eprints.usq.edu.au/id/eprint/5654

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