Exploiting Cluster Analysis for Constructing Multi-dimensional Histograms on Both Static and Evolving Data

  • Filippo Furfaro
  • Giuseppe M. Mazzeo
  • Cristina Sirangelo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3896)

Abstract

Density-based clusterization techniques are investigated as a basis for constructing histograms in multi-dimensional scenarios, where traditional techniques fail in providing effective data synopses. The main idea is that locating dense and sparse regions can be exploited to partition the data into homogeneous buckets, preventing dense and sparse regions from being summarized into the same aggregate data. The use of clustering techniques to support the histogram construction is investigated in the context of either static and dynamic data, where the use of incremental clustering strategies is mandatory due to the inefficiency of performing the clusterization task from scratch at each data update.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Filippo Furfaro
    • 1
  • Giuseppe M. Mazzeo
    • 1
  • Cristina Sirangelo
    • 1
  1. 1.DEISUniversity of CalabriaRendeItaly

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