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.
A Rough Set Approach to Spatio-temporal Outlier Detection
ALBANESE, Alessia;PETROSINO, Alfredo
2011-01-01
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
