Issues on Clustering and Data Gridding

  • Jukka Heikkonen
  • Domenico Perrotta
  • Marco Riani
  • Francesca Torti
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

This contribution addresses clustering issues in presence of densely populated data points with high degree of overlapping. In order to avoid the disturbing effects of high dense areas we suggest a technique that selects a point in each cell of a grid defined along the Principal Component axes of the data. The selected sub-sample removes the high density areas while preserving the general structure of the data. Once the clustering on the gridded data is produced, it is easy to classify the rest of the data with reliable and stable results. The good performance of the approach is shown on a complex dataset coming from international trade data.

Keywords

European Union Gaussian Mixture Model Gridded Data Multivariate Gaussian Distribution Disturbing Effect 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jukka Heikkonen
    • 1
  • Domenico Perrotta
    • 2
  • Marco Riani
    • 3
  • Francesca Torti
    • 4
  1. 1.Department of Information TechnologyUniversity of TurkuTurkuFinland
  2. 2.EC Joint Research Centre, Ispra siteIspraItaly
  3. 3.University of ParmaParmaItaly
  4. 4.University of Milano BicoccaMilanItaly

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