Please use this identifier to cite or link to this item:
|Title:||Finding outliers at multiple scales|
Complete spatial randomness
|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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Feb 22, 2018
WEB OF SCIENCETM
checked on Jan 24, 2018
checked on Feb 19, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.