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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© 2011 Springer-Verlag Berlin Heidelberg
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Albanese, A., Petrosino, A. (2011). A Non Parametric Approach to the Outlier Detection in Spatio–Temporal Data Analysis. In: D'Atri, A., Ferrara, M., George, J., Spagnoletti, P. (eds) Information Technology and Innovation Trends in Organizations. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2632-6_12
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DOI: https://doi.org/10.1007/978-3-7908-2632-6_12
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Publisher Name: Physica-Verlag HD
Print ISBN: 978-3-7908-2631-9
Online ISBN: 978-3-7908-2632-6
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