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Application of Self-Organizing Maps to Outlier Identification and Estimation of Missing Data

  • Mariusz Grabowski
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Kohonen’s self-organizing maps (SOM) belong to the group of artificial neural network methods that are the most frequently applied to data analysis. The most common applications of SOM are multidimensional data visualization and huge data sets clustering. Some characteristics of SOM make this method interesting also in other aspects of data analysis. The following paper presents the possibility of SOM application to outliers identification and missing data estimation.

Keywords

Neural Networks Self-organizing Maps Outliers Missing Data 

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References

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Copyright information

© Springer-Verlag Berlin · Heidelberg 1998

Authors and Affiliations

  • Mariusz Grabowski
    • 1
  1. 1.Department of Computer ScienceCracow University of EconomicsCracowPoland

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