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

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Part of the Lecture Notes in Computer Science book series (LNISA,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.

Keywords

  • Storage Space
  • Range Query
  • Incremental Approach
  • Core Point
  • Region Query

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.

This work was supported by a grant from the Italian Research Project FIRB “Grid.it – Enabling ICT Platforms for Distributed High-Performance Computational Grids”, funded by MIUR and coordinated by the National Research Council (CNR).

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Furfaro, F., Mazzeo, G.M., Sirangelo, C. (2006). Exploiting Cluster Analysis for Constructing Multi-dimensional Histograms on Both Static and Evolving Data. In: , et al. Advances in Database Technology - EDBT 2006. EDBT 2006. Lecture Notes in Computer Science, vol 3896. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11687238_28

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  • DOI: https://doi.org/10.1007/11687238_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-32960-2

  • Online ISBN: 978-3-540-32961-9

  • eBook Packages: Computer ScienceComputer Science (R0)