FINDING OUTLIERS AT MULTIPLE SCALES
Tianmin Hu () and
Sam Yuan Sung
Additional contact information
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
References: View complete reference list from CitEc
Citations:
Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219622005001507
Access to full text is restricted to subscribers
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijitdm:v:04:y:2005:i:02:n:s0219622005001507
Ordering information: This journal article can be ordered from
DOI: 10.1142/S0219622005001507
Access Statistics for this article
International Journal of Information Technology & Decision Making (IJITDM) is currently edited by Yong Shi
More articles in International Journal of Information Technology & Decision Making (IJITDM) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().