SODIT: An innovative system for outlier detection using multiple localized thresholding and interactive feedback

Zhang, Ji and Wang, Hua and Tao, Xiaohui and Sun, Lili (2013) SODIT: An innovative system for outlier detection using multiple localized thresholding and interactive feedback. In: 29th IEEE International Conference on Data Engineering (ICDE 2013), 8-11 Apr 2013, Brisbane, Australia.


Outlier detection is an important long-standing research problem in data mining and has enjoyed applications in a wide range of applications in business, engineering, biology and security, etc. However, the traditional outlier detection methods inevitably need to use different parameters for detection such as those used to specify the distance or density cutoff for distinguish outliers from normal data points. Using the trial and error approach, the traditional outlier detection methods are rather tedious in parameter tuning. In this demo proposal, we introduce an innovative outlier detection system, called SODIT, that uses localized thresholding to assist the value specification of the thresholds that reflect closely the local data distribution. In addition, easy-to-use user feedback are employed to further facilitate the determination of optimal parameter values. SODIT is able to make outlier detection much easier to operate and produce more accurate, intuitive and informative results than before.

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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2013 IEEE. Permanent restricted access to published version due to publisher copyright policy.
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 15 Apr 2014 10:49
Last Modified: 23 Feb 2015 00:32
Uncontrolled Keywords: data mining; innovative system; interactive feedback; local data distribution; localized thresholding; outlier detection; parameter tuning; value specification
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
08 Information and Computing Sciences > 0805 Distributed Computing > 080501 Distributed and Grid Systems
01 Mathematical Sciences > 0102 Applied Mathematics > 010203 Calculus of Variations, Systems Theory and Control Theory
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1109/ICDE.2013.6544945

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