SODIT: An innovative system for outlier detection using multiple localized thresholding and interactive feedback
Paper
Paper/Presentation Title | SODIT: An innovative system for outlier detection using multiple localized thresholding and interactive feedback |
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Presentation Type | Paper |
Authors | Zhang, Ji (Author), Wang, Hua (Author), Tao, Xiaohui (Author) and Sun, Lili (Author) |
Journal or Proceedings Title | Proceedings of the 29th IEEE International Conference on Data Engineering (ICDE 2013) |
ERA Conference ID | 43307 |
Number of Pages | 4 |
Year | 2013 |
Place of Publication | Piscataway, NJ. United States |
ISBN | 9781467349093 |
9781467349086 | |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ICDE.2013.6544945 |
Web Address (URL) of Paper | http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6544945 |
Conference/Event | 29th IEEE International Conference on Data Engineering (ICDE 2013) |
International Conference on Data Engineering | |
Event Details | International Conference on Data Engineering ICDE Rank A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A |
Event Details | 29th IEEE International Conference on Data Engineering (ICDE 2013) Event Date 08 to end of 11 Apr 2013 Event Location Brisbane, Australia |
Abstract | 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. |
Keywords | data mining; innovative system; interactive feedback; local data distribution; localized thresholding; outlier detection; parameter tuning; value specification |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
460605. Distributed systems and algorithms | |
490103. Calculus of variations, mathematical aspects of systems theory and control theory | |
Public Notes | © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Byline Affiliations | Department of Mathematics and Computing |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q24x7/sodit-an-innovative-system-for-outlier-detection-using-multiple-localized-thresholding-and-interactive-feedback
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