Dynamic Hierarchical Compact Clustering Algorithm

  • Reynaldo Gil-García
  • José M. Badía-Contelles
  • Aurora Pons-Porrata
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)


In this paper we introduce a general framework for hierarchical clustering that deals with both static and dynamic data sets. From this framework, different hierarchical agglomerative algorithms can be obtained, by specifying an inter-cluster similarity measure, a subgraph of the β-similarity graph, and a cover algorithm. A new clustering algorithm called Hierarchical Compact Algorithm and its dynamic version are presented, which are specific versions of the proposed framework. Our evaluation experiments on several standard document collections show that this algorithm requires less computational time than standard methods in dynamic data sets while achieving a comparable or even better clustering quality. Therefore, we advocate its use for tasks that require dynamic clustering, such as information organization, creation of document taxonomies and hierarchical topic detection.


Cluster Algorithm Document Cluster Hierarchical Cluster Algorithm Hierarchical Cluster Method Cover 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 2005

Authors and Affiliations

  • Reynaldo Gil-García
    • 1
  • José M. Badía-Contelles
    • 2
  • Aurora Pons-Porrata
    • 1
  1. 1.Center of Pattern Recognition and Data MiningUniversidad de OrienteSantiago de CubaCuba
  2. 2.Universitat Jaume ICastellónSpain

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