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Determination of Similarity Threshold in Clustering Problems for Large Data Sets

  • Guillermo Sánchez-Díaz
  • José F. Martínez-Trinidad
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2905)

Abstract

A new automatic method based on an intra-cluster criterion, to obtain a similarity threshold that generates a well-defined clustering (or near to it) for large data sets, is proposed. This method uses the connected component criterion, and it neither calculates nor stores the similarity matrix of the objects in main memory. The proposed method is focused on unsupervised Logical Combinatorial Pattern Recognition approach. In addition, some experimentations of the new method with large data sets are presented.

Keywords

Similarity Matrix Cluster Problem Similarity Threshold Unsupervised Classification Cluster Criterion 
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 2003

Authors and Affiliations

  • Guillermo Sánchez-Díaz
    • 1
  • José F. Martínez-Trinidad
    • 2
  1. 1.Center of Technologies Research on Information and SystemsThe Autonomous University of the Hidalgo StatePachucaMexico
  2. 2.National Institute of Astrophysics, Optics and ElectronicsPueblaMexico

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