SLOM: a new measure for local spatial outliers |
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Authors: | Sanjay Chawla Pei Sun |
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Affiliation: | (1) School of Information Technologies, University of Sydney, New South Wales, Australia |
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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.
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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Keywords: | Spatial local outlier Spatial neighbourhood Oscillating parameter R-trees index Complexity |
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