Grid-ODF: detecting outliers effectively and efficiently in large multi-dimensional databases

Wang, Wei and Zhang, Ji and Wang, Hai (2005) Grid-ODF: detecting outliers effectively and efficiently in large multi-dimensional databases. In: 2005 IEEE International Conference on Computational Intelligence and Security (CIS'05), 15-19 Dec 2005, Xi'an, China.

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Abstract

[Abstract]: In this paper, we will propose a novel outlier mining algorithm, called Grid-ODF, that takes into account both the local and global perspectives of outliers for effective detection. The notion of Outlying Degree Factor (ODF), that reflects the factors of both the density and distance, is introduced to rank outliers. A grid structure partitioning the data space is employed to enable Grid- ODF to be implemented efficiently. Experimental results show that Grid-ODF outperforms existing outlier detection algorithms such as LOF and KNN-distance in terms of effectiveness and efficiency.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Copyright 2005 Springer. This is the author's version of a paper published in the series Lecture Notes in Artificial Intelligence, v. 3801, 2005. Deposited in accordance with the copyright policy of the publisher, Springer.
Depositing User: Dr Ji Zhang
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 02 Sep 2009 05:35
Last Modified: 02 Jul 2013 23:23
Uncontrolled Keywords: outliers; Grid-ODF; outlying degree factor
Fields of Research (FOR2008): 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/5644

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