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

Chapter

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.

References

  1. 1.
    D. Birant, A. Kut, “Spatio-Temporal Outlier Detection in Large Databases”, Journal of Computing and Information Technology, vol. 14, no. 4, pp. 291–297, 2006.Google Scholar
  2. 2.
    T. Cheng, Z. Li, “A Multiscale Approach for Spatio-Temporal Outlier Detection”, Transactions in GIS, vol. 10, no. 2, pp. 253–263, march 2006.Google Scholar
  3. 3.
    E. M. Knorr, T.Ng. Raymond, “A Unified Notion of Outliers: Properties and Computation”, 3 rd International Conference on Knowledge Discovery and Data Mining Proceedings, pp. 219–222, 1997.Google Scholar
  4. 4.
    E. Knorr and R. Ng, Algorithms for Mining Distance-Based Outliers in Large Datasets, Proc. Intl Conf. Very Large Databases (VLDB 98), pp. 392–403, 1998.Google Scholar
  5. 5.
    Ng RT, Han J. Efficient and Effective Clustering Methods for Spatial Data Mining, In: Proc. 20th Int. Conf. on Very Large Data Bases, Santiago, Chile; 1994. p. 144–155.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Department of Applied ScienceUniversity of Naples ParthenopeNaplesItaly

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