Rough sets, Kernel set and spatio-temporal outlier detection

Abstract

Nowadays, the high availability of data gathered from wireless sensor networks and telecommunication systems, has drawn the attention of researchers on the problem of extracting knowledge from spatio-temporal data. Detecting outliers which are grossly different from or inconsistent with the remaining spatio-temporal dataset is a major challenge in real-world knowledge discovery and data mining applications. In this paper, we deal with the outlier detection problem in spatio-temporal data and describe a rough set approach that finds the top outliers in an unlabeled spatio-temporal dataset. The proposed method, called Rough Outlier Set Extraction (ROSE), relies on a rough set theoretic representation of the outlier set using the rough set approximations, i.e. lower and upper approximations. We have also introduced a new set, named Kernel Set, that is a subset of the original dataset, which is able to describe the original dataset both in terms of data structure and of obtained results. Experimental results on real world datasets demonstrate the superiority of ROSE, both in terms of some quantitative indices and outliers detected, over those obtained by various clustering algorithms. It is also demonstrated that the kernel set is able to detect the same outliers set but with less computational time.