PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data
Ke Zhang, Marcus Hutter and Warren Jin
In: 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 27-30 April 2009, Bangkok, Thailand.

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. Properties of LDOF are theoretically analysed including LDOF's lower bound and its false-detection probability, as well as parameter settings. In order to facilitate parameter settings in real-world applications, we employ a top-n technique in our outlier detection approach, where only the objects with the highest LDOF values are regarded as outliers. Compared to conventional approaches (such as top-n KNN and top-n LOF), our method top-n LDOF is more effective at detecting outliers in scattered data. It is also easier to set parameters, since its performance is relatively stable over a large range of parameter values, as illustrated by experimental results on both real-world and synthetic datasets.

PDF (http://www.hutter1.net/ai/ldof.pdf) - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:5845
Deposited By:Marcus Hutter
Deposited On:08 March 2010