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SLOM: a new measure for local spatial outliers

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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.

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Authors and Affiliations

  1. School of Information Technologies, University of Sydney, New South Wales, Australia

    Sanjay Chawla & Pei Sun

Authors
  1. Sanjay Chawla
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  2. Pei Sun
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Sanjay Chawla is a Senior Lecturer in the School of Information Technologies at the University of Sydney. His research interests span the area of data mining and spatial database management. He is a co-author of the textbook “Spatial Databases: A Tour”, which is published by Prentice Hall. His research work has appeared in leading publications, including IEEE Transaction on Knowledge and Data Engineering and GeoInformatica. He received his Ph.D. in Mathematics from the University of Tennessee, USA.

Pei Sun is currently a Ph.D. student in the School of Information Technology, Sydney University, Australia. His research interests include data mining and spatial database. He received his M.E. degree from the University of New South Wales, Sydney, Australia, in 2002 and a B.E. degree from Beijing Forestry University, China, in 1990.

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Chawla, S., Sun, P. SLOM: a new measure for local spatial outliers. Knowl Inf Syst 9, 412–429 (2006). https://doi.org/10.1007/s10115-005-0200-2

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