State of the Art in Patterns for Point Cluster Analysis

  • Laurent Etienne
  • Thomas Devogele
  • Gavin McArdle
Conference paper

DOI: 10.1007/978-3-319-09144-0_18

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8579)
Cite this paper as:
Etienne L., Devogele T., McArdle G. (2014) State of the Art in Patterns for Point Cluster Analysis. In: Murgante B. et al. (eds) Computational Science and Its Applications – ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol 8579. Springer, Cham


Nowadays, an abundance of sensors are used to collect very large datasets containing spatial points which can be mined and analyzed to extract meaningful patterns. In this article, we focus on different techniques used to summarize and visualize 2D point clusters and discuss their relative strengths. This article focuses on patterns which describe the dispersion of data around a central tendency. These techniques are particularly beneficial for detecting outliers and understanding the spatial density of point clusters.


Point clusters Oriented Spatio-Temporal Box Plot Bagplot Quelplot Outlier detection Spatio-temporal patterns 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Laurent Etienne
    • 1
  • Thomas Devogele
    • 2
  • Gavin McArdle
    • 3
  1. 1.French Naval Academy Research InstituteBrestFrance
  2. 2.Laboratoire d’informatiqueUniversité François Rabelais de ToursBloisFrance
  3. 3.National Centre for GeocomputationNational University of Ireland MaynoothMaynoothIreland

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