Adaptive Clustering Algorithms

  • Alina Câmpan
  • Gabriela Şerban
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4013)


This paper proposes an adaptive clustering approach. We focus on re-clustering an object set, previously clustered, when the feature set characterizing the objects increases. We have developed adaptive extensions for two traditional clustering algorithms (k-means and Hierarchical Agglomerative Clustering). These extensions can be used for adjusting a clustering, that was established by applying the corresponding non-adaptive clustering algorithm before the feature set changed. We aim to reach the result more efficiently than applying the corresponding non-adaptive algorithm starting from the current clustering or from scratch. Experiments testing the method’s efficiency are also reported.


Cluster Algorithm Information Gain Cluster Core Object Orient Database Cluster Tendency 
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 2006

Authors and Affiliations

  • Alina Câmpan
    • 1
  • Gabriela Şerban
    • 1
  1. 1.Department of Computer Science“Babeş-Bolyai” UniversityCluj-NapocaRomania

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