A Non Parametric Approach to the Outlier Detection in Spatio–Temporal Data Analysis
Alessia Albanese, Alfredo Petrosino · 2011
Detecting outliers which are grossly different from or inconsistent with the remaining spatio–temporal data set is a major challenge in real-world knowledge discovery and data mining applications. In this paper, we face the outlier detection problem in spatio–temporal data. The proposed non parametric method rely on a new fusion approach able to discover outliers according to the spatial and temporal features, at the same time: the user can decide the importance to give to both components (spatial and temporal) depending upon the kind of data to be analyzed and/or the kind of analysis to be performed. Experiments on synthetic and real world data sets to evaluate the effectiveness of the approach are reported.