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Erschienen in:
2009 | OriginalPaper | Buchkapitel
A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data
verfasst von : Ke Zhang, Marcus Hutter, Huidong Jin
Erschienen in: Advances in Knowledge Discovery and Data Mining
Verlag: Springer Berlin Heidelberg
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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 neighbours to determine the degree to which the object deviates from its neighbourhood. We present theoretical bounds on LDOF’s false-detection probability. Experimentally, LDOF compares favorably to classical KNN and LOF based outlier detection. In particular it is less sensitive to parameter values.