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

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.