Global k-Means with Similarity Functions

  • Saúl López-Escobar
  • J. A. Carrasco-Ochoa
  • J. Fco. Martínez-Trinidad
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)

Abstract

The k-means algorithm is a frequently used algorithm for solving clustering problems. This algorithm has the disadvantage that it depends on the initial conditions, for that reason, the global k-means algorithm was proposed to solve this problem. On the other hand, the k-means algorithm only works with numerical features. This problem is solved by the k-means algorithm with similarity functions that allows working with qualitative and quantitative variables and missing data (mixed and incomplete data). However, this algorithm still depends on the initial conditions. Therefore, in this paper an algorithm to solve the dependency on initial conditions of the k-means algorithm with similarity functions is proposed, our algorithm is tested and compared against k-means algorithm with similarity functions.

Keywords

Objective Function Local Search Similarity Function Optimal Position Cluster Problem 
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.

References

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    Blake, C.L., Merz, C.J.: UCI repository of machine learning databases, University of California, Irvine, Departament of Information and Computer Sciences (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Saúl López-Escobar
    • 1
  • J. A. Carrasco-Ochoa
    • 1
  • J. Fco. Martínez-Trinidad
    • 1
  1. 1.National Institute for AstrophysicsOptics and ElectronicsSta. Ma. TonantzintlaMéxico

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