Fully Dynamic Clustering of Metric Data Sets

  • Stefano Lodi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2405)


The goal of cluster analysis [10] is to find homogeneous groups, or clusters, in data. Homogeneity is often made precise by means of a dissimilarity function on objects, having low values at pairs of objects in one cluster. Cluster analysis has also been investigated in data mining [5], emphasising efficiency on data sets larger than main memory [4,6,8,9,16]. More recently, the growing importance of multimedia and transactional databases has stimulated interest in metric clustering, i.e. when dissimilarity satisfies the triangular inequality.


External Memory Dynamic Cluster Dynamic Algorithm Connectivity Query Euler Tour 
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 2002

Authors and Affiliations

  • Stefano Lodi
    • 1
  1. 1.Department of Electronics, Computer Science, and SystemsUniversity of BolognaBolognaItaly

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