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

Authors:
Wei Wang;Ji Zhang;Hai Wang
Affiliations:
College of Educational Science, Nanjing Normal University, China;Falculty of Computer Science, Dalhousie University, Halifax, Canada;Sobey School of Business, Saint Mary’s University, Halifax, Canada
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Year:
2005

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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 ofOutlying 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.