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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Official URL: http://www.comp.hkbu.edu.hk/~cis05/home/
[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.
|Item Type:||Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)|
|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.|
|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|
|Subjects:||280000 Information, Computing and Communication Sciences|
|Socio-Economic Objective (SEO2008):||UNSPECIFIED|
|Deposited On:||02 Sep 2009 15:35|
|Last Modified:||24 Nov 2011 11:12|
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