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

Authors:
Ke Zhang;Marcus Hutter;Huidong Jin
Affiliations:
RSISE, Australian National University,;RSISE, Australian National University, and National ICT Australia (NICTA), Canberra Lab, ACT, Australia;RSISE, Australian National University, and National ICT Australia (NICTA), Canberra Lab, ACT, Australia and CSIRO Mathematical and Information Sciences, Acton ACT 2601, Australia
Venue:
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Year:
2009

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Abstract

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.