Improving the Efficiency and Efficacy of the K-means Clustering Algorithm Through a New Convergence Condition

  • Joaquín Pérez O
  • Rodolfo Pazos R
  • Laura Cruz R
  • Gerardo Reyes S
  • Rosy Basave T
  • Héctor Fraire H
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4707)


Clustering problems arise in many different applications: machine learning, data mining, knowledge discovery, data compression, vector quantization, pattern recognition and pattern classification. One of the most popular and widely studied clustering methods is K-means. Several improvements to the standard K-means algorithm have been carried out, most of them related to the initial parameter values. In contrast, this article proposes an improvement using a new convergence condition that consists of stopping the execution when a local optimum is found or no more object exchanges among groups can be performed. For assessing the improvement attained, the modified algorithm (Early Stop K-means) was tested on six databases of the UCI repository, and the results were compared against SPSS, Weka and the standard K-means algorithm. Experimentally Early Stop K-means obtained important reductions in the number of iterations and improvements in the solution quality with respect to the other algorithms.


Convergence Condition Early Stop Initial Centroid Diabetes Database Heart Disease Dataset 
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 2007

Authors and Affiliations

  • Joaquín Pérez O
    • 1
  • Rodolfo Pazos R
    • 1
  • Laura Cruz R
    • 2
  • Gerardo Reyes S
    • 1
  • Rosy Basave T
    • 1
  • Héctor Fraire H
    • 2
  1. 1.Centro Nacional de Investigación y Desarrollo Tecnológico (CENIDET) 
  2. 2.Instituto Tecnológico de Ciudad Madero 

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