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

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


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.


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
    Email author
  • 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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