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A New Local Distance-based Outlier Detection Approach for Scattered Real-World Data

Zhang, Ke; Hutter, Marcus; Jin, Huidong


Detecting outliers which are grossly different from or inconsistent with the remaining dataset is a major challenge in real-world KDD applications. Existing outlier detection methods are ineffective on scattered real-world datasets due to implicit data patterns and parameter setting issues. We define a novel Local Distance-based Outlier Factor (LDOF) to measure the outlier-ness of objects in scattered datasets which addresses these issues. LDOF uses the relative location of an object to its...[Show more]

CollectionsANU Research Publications
Date published: 2009
Type: Conference paper
Source: Proceedings of the 13th Asia-Pacific Conference on Knowledge Discovery and Data Mining (PAKDD'09)
DOI: 10.1007/978-3-642-01307-2_84


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