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A Heuristic Automatic Clustering Method Based on Hierarchical Clustering

  • François LaPlanteEmail author
  • Nabil Belacel
  • Mustapha Kardouchi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8946)

Abstract

We propose a clustering method which produces valid results while automatically determining an optimal number of clusters. The proposed method achieves these results with minimal user input, of which none pertains to a number of clusters. Our method’s effectiveness in clustering, including its ability to produce valid results on data sets presenting nested or interlocking shapes, is demonstrated and compared with cluster validity analysis to other methods to which a known optimal number of clusters is provided, and to other automatic clustering methods. Depending on the particularities of the data set used, our method has produced results which are roughly equivalent or better than those of the compared methods.

Keywords

Data-mining Automatic clustering Unsupervised learning 

Notes

Acknowledgements

We gratefully acknowledge the support from NBIF’s (RAI 2012-047) New Brunswick Innovation Funding granted to Dr. Nabil Belacel.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • François LaPlante
    • 1
    Email author
  • Nabil Belacel
    • 2
  • Mustapha Kardouchi
    • 1
  1. 1.Université de MonctonMonctonCanada
  2. 2.National Research Council - Information and Communications TechnologiesMonctonCanada

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