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Comparison of K-Means and Fuzzy C-Means Data Mining Algorithms for Analysis of Management Information: An Open Source Case

  • Angélica Urrutia
  • Hector Valdes
  • José Galindo
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 217)

Abstract

This research presents the knowledge discovery using Data Mining from the organization and with a KPI management point of view. The stages presented here are based on techniques and Data Mining models, with emphasis on clustering techniques, such as the C-MEANS algorithm. We both consider the classic and fuzzy perspectives, namely Fuzzy C-MEANS and K-MEANS, and then compare the results based on the level of support which each algorithm provides to information management. The CRISP-DM methodology is used in our implementation, which is then applied to three case studies.

Keywords

Fuzzy C-MEANS algorithm K-MEANS Data Mining management data analysis 

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Angélica Urrutia
    • 1
  • Hector Valdes
    • 1
  • José Galindo
    • 2
  1. 1.TRICAHUE Database GroupUniversidad Católica de MauleMauleChile
  2. 2.Dpto. de Lenguajes y Ciencias de la ComputaciónUniversidad de MálagaMálagaSpain

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