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Attainment of K-Means Algorithm using Hellinger distance

Author

Listed:
  • Stancu Ana-Maria Ramona

    (“Dimitrie Cantemir†Christian University)

  • Cristescu Marian Pompiliu

    (“Lucian Blaga†University of Sibiu)

  • Stoica Liviu Constantin

    (Academy of Economic Studies, Bucharest)

Abstract

In this article in the first part I will begin with an introduction to unsupervised learning methods, focusing on the K-Means clustering algorithm, which is achieved with the help of the Euclidian distance. In the second part we modified the K-Means algorithm, that is, it was achieved with the help of the Hellinger distance, after which the clustering time was compared and a parallel was made between the two algorithms (the K-Means algorithm achieved with the Euclidean distance and the K-Means algorithm achieved with Hellinger distance). As a result of the two algorithms I found that the number of groups is the same, and the number of iterations is different.

Suggested Citation

  • Stancu Ana-Maria Ramona & Cristescu Marian Pompiliu & Stoica Liviu Constantin, 2017. "Attainment of K-Means Algorithm using Hellinger distance," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(2), pages 324-329, December.
    • Handle: RePEc:ovi:oviste:v:xvii:y:2017:i:2:p:324-329
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    More about this item

    Keywords

    algorithm; cluster; distance; iteration; group;
    All these keywords.

    JEL classification:

    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D

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