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Published in:
2005 | OriginalPaper | Chapter
Grid-ODF: Detecting Outliers Effectively and Efficiently in Large Multi-dimensional Databases
Authors : Wei Wang, Ji Zhang, Hai Wang
Published in: Computational Intelligence and Security
Publisher: Springer Berlin Heidelberg
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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.