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

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2011

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11367/24122
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