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Knowledge and Information Systems

, Volume 9, Issue 4, pp 412–429 | Cite as

SLOM: a new measure for local spatial outliers

  • Sanjay Chawla
  • Pei Sun
Regular Paper

Abstract

We propose a measure, spatial local outlier measure (SLOM), which captures the local behaviour of datum in their spatial neighbourhood. With the help of SLOM, we are able to discern local spatial outliers that 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 suppresses the reporting of outliers in highly unstable areas, where data are 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 that show that our approach is novel and scalable to large datasets.

Spatial local outlier Spatial neighbourhood Oscillating parameter R-trees index Complexity 

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Copyright information

© Springer-Verlag London Limited 2005

Authors and Affiliations

  • Sanjay Chawla
    • 1
  • Pei Sun
    • 1
  1. 1.School of Information TechnologiesUniversity of SydneyNew South WalesAustralia

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