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]: 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)|
|Publisher:||Morgan Kaufmann Publishers Inc.|
|Item Status:||Live Archive|
|Additional Information (displayed to public):||Author's version deposited in accordance with the copyright policy of the publisher.|
|Depositing User:||Dr Ji Zhang|
|Faculty / Department / School:||Historic - Faculty of Sciences - Department of Maths and Computing|
|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 (FoR):||08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining|
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