On local spatial outliers

Pei Sun, Sanjay Chawla

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

92 Scopus citations

Abstract

We propose a measure, Spatial Local Outlier Measure (SLOM) which captures the local behaviour of datum in their spatial neighborhood. With the help of SLOM we are able to discern local spatial outliers which are usually missed by global techniques like "three standard deviations away from the mean". Furthermore the measure takes into account the local stability around a data point and supresses the reporting of outliers in highly unstable areas, where data is too heterogeneous and the notion of outliers is not meaningful. We prove several properties of SLOM and report experiments on synthetic and real data sets which show that our approach is novel and scalable to large data sets.

Original languageEnglish
Title of host publicationProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004
EditorsR. Rastogi, K. Morik, M. Bramer, X. Wu
Pages209-216
Number of pages8
StatePublished - 2004
Externally publishedYes
EventProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004 - Brighton, United Kingdom
Duration: Nov 1 2004Nov 4 2004

Publication series

NameProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004

Conference

ConferenceProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004
Country/TerritoryUnited Kingdom
CityBrighton
Period11/1/0411/4/04

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