FINDING OUTLIERS AT MULTIPLE SCALES

Tianmin Hu () and Sam Yuan Sung
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Tianmin Hu: School of Mathematics and Information Technology, HanShan Normal University, Chaozhou, GuangDong, China 521041, China
Sam Yuan Sung: Department of Computer Science, National University of Singapore, 3 Science Drive 2, Singapore 117543, Singapore

International Journal of Information Technology & Decision Making (IJITDM), 2005, vol. 04, issue 02, 251-262

Abstract: Outlier detection targets those exceptional data whose pattern is rare and lie in low density regions. In this paper, under the assumption of complete spatial randomness inside clusters, we propose an MDV (Multi-scale Deviation of the Volume) approach to identifying outliers. In addition to assigning an outlier score for each object, it directly outputs a crisp outlier set. It also offers a plot showing the data structure in every object's vicinity, which is useful in explaining why it may be outlying. Finally, the effectiveness of MDV is demonstrated with both artificial and real datasets.

Keywords: Outlier detection; clustering; complete spatial randomness; knowledge discovery (search for similar items in EconPapers)
Date: 2005
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DOI: 10.1142/S0219622005001507

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