Please use this identifier to cite or link to this item: https://doi.org/10.1142/S0219622005001507
Title: Finding outliers at multiple scales
Authors: Hu, T.
Sung, S.Y. 
Keywords: Clustering
Complete spatial randomness
Knowledge discovery
Outlier detection
Issue Date: 2005
Source: Hu, T., Sung, S.Y. (2005). Finding outliers at multiple scales. International Journal of Information Technology and Decision Making 4 (2) : 251-262. ScholarBank@NUS Repository. https://doi.org/10.1142/S0219622005001507
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. © World Scientific Publishing Company.
Source Title: International Journal of Information Technology and Decision Making
URI: http://scholarbank.nus.edu.sg/handle/10635/39316
ISSN: 02196220
DOI: 10.1142/S0219622005001507
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