A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data
Date
2009
Authors
Zhang, Ke
Hutter, Marcus
Jin, Huidong
Journal Title
Journal ISSN
Volume Title
Publisher
Springer Verlag
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.
Description
Keywords
local outlier, scattered data, k-distance, KNN, LOF, LDOF
Citation
Collections
Source
Type
Conference paper
Book Title
Advances in knowledge discovery and data mining 13th Pacific-Asia Conference, PAKDD 2009, Bangkok, Thailand, April 27-30, 2009 ; proceedings
Entity type
Access Statement
License Rights
DOI
10.1007/978-3-642-01307-2_84