Continuously Mining Sliding Window Trend Clusters in a Sensor Network

  • Annalisa Appice
  • Donato Malerba
  • Anna Ciampi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7447)

Abstract

The trend cluster discovery retrieves areas of spatially close sensors which measure a numeric random field having a prominent data trend along a time horizon. We propose a computation preserving algorithm which employees an incremental learning strategy to continuously maintain sliding window trend clusters across a sensor network. Our proposal reduces the amount of data to be processed and saves the computation time as a consequence. An empirical study proves the effectiveness of the proposed algorithm to take under control computation cost of detecting sliding window trend clusters.

Keywords

Sensor Network Time Horizon Spatial Cluster Inverse Distance Weighting Sensor Reading 
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 2012

Authors and Affiliations

  • Annalisa Appice
    • 1
  • Donato Malerba
    • 1
  • Anna Ciampi
    • 1
  1. 1.Dipartimento di InformaticaUniversità degli Studi di Bari Aldo MoroBariItaly

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