Improving the Dynamic Hierarchical Compact Clustering Algorithm by Using Feature Selection

  • Reynaldo Gil-García
  • Aurora Pons-Porrata
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6419)


Feature selection has improved the performance of text clustering. In this paper, a local feature selection technique is incorporated in the dynamic hierarchical compact clustering algorithm to speed up the computation of similarities. We also present a quality measure to evaluate hierarchical clustering that considers the cost of finding the optimal cluster from the root. The experimental results on several benchmark text collections show that the proposed method is faster than the original algorithm while achieving approximately the same clustering quality.


Feature Selection Cluster Quality Optimal Cluster Document Cluster Hierarchical Cluster Algorithm 
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 2010

Authors and Affiliations

  • Reynaldo Gil-García
    • 1
  • Aurora Pons-Porrata
    • 1
  1. 1.Center for Pattern Recognition and Data MiningUniversidad de OrienteSantiago de CubaCuba

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