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

In: Information Technology and Innovation Trends in Organizations

Author

Listed:
  • Alessia Albanese

    (University of Naples Parthenope)

  • Alfredo Petrosino

    (University of Naples Parthenope)

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.

Suggested Citation

  • Alessia Albanese & Alfredo Petrosino, 2011. "A Non Parametric Approach to the Outlier Detection in Spatio–Temporal Data Analysis," Springer Books, in: Alessandro D'Atri & Maria Ferrara & Joey F. George & Paolo Spagnoletti (ed.), Information Technology and Innovation Trends in Organizations, pages 101-108, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2632-6_12
    DOI: 10.1007/978-3-7908-2632-6_12
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