A Non Parametric Approach to the Outlier Detection in Spatio–Temporal Data Analysis

Alessia Albanese () and Alfredo Petrosino ()
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Alessia Albanese: University of Naples Parthenope
Alfredo Petrosino: University of Naples Parthenope

A chapter in Information Technology and Innovation Trends in Organizations, 2011, pp 101-108 from Springer

Abstract: 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.

Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2632-6_12

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DOI: 10.1007/978-3-7908-2632-6_12

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