State of the Art in Patterns for Point Cluster Analysis

  • Laurent Etienne
  • Thomas Devogele
  • Gavin McArdle
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8579)

Abstract

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.

Keywords

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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Becketti, S., Gould, W.: Rangefinder Box Plots, A Note, The American Statistician (1987)Google Scholar
  2. 2.
    Berkhin, P.: A survey of clustering data mining techniques. In: Grouping Multidimensional Data, pp. 25–71. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Devogele, T., Etienne, L., Ray, C.: Mobility Applications, Maritime Applications. In: Renso, C., Spaccapietra, S., Zimanyi, E. (eds.) Mobility Data: Modeling, Management, and Understanding, Part III, pp. 224–243. Cambridge Press (2013)Google Scholar
  4. 4.
    Getz, W.M., Wilmers, C.C.: A local nearest-neighbor convex-hull construction of home ranges and utilization distributions. Ecography 27, 489–505 (2004)CrossRefGoogle Scholar
  5. 5.
    Goldberg, K., Iglewicz, B.: Bivariate Extensions of the Box Plot. American Statistician 34(3), 307–320 (1992)Google Scholar
  6. 6.
    Jain, A., Murty, M., Flynn, P.: Data clustering: a review. ACM Computing Surveys (CSUR) 31(3), 264–323 (1999)CrossRefGoogle Scholar
  7. 7.
    Kenward, R.: Wildlife radio tagging. Academic Press, Inc., London (1987)Google Scholar
  8. 8.
    Lefever, D.W.: Measuring Geographic Concentration by Means of the Standard Deviational Ellipse. American Journal of Sociology 32(1), 88–94 (1926)CrossRefGoogle Scholar
  9. 9.
    Mohr, C.O.: Table of equivalent populations of North American small mammals. American Midland Naturalist 37(1), 223–249 (1947)CrossRefGoogle Scholar
  10. 10.
    Pearson, K.: On Lines and Planes of Closest Fit to Systems of Points in Space. Philosophical Magazine 2(11), 559–572 (1901)CrossRefGoogle Scholar
  11. 11.
    Potter, K., Hagen, H., Kerren, A., Dannenmann, P.: Methods for presenting statistical information: The box plot. In: Visualization of Large and Unstructured Data Sets (LNI), vol. 4, pp. 97–106 (2006)Google Scholar
  12. 12.
    Rousseeuw, P., Ruts, I., Tukey, J.: The bagplot: a bivariate boxplot. The American Statistician 53(4), 382–387 (1999)Google Scholar
  13. 13.
    Small, C.A.: Survey of Multidimensional Medians. International Statistical Review 58(3), 263–277 (1990)CrossRefGoogle Scholar
  14. 14.
    Tukey, J.: Exploratory Data Analysis. Addison-Wesley (1977)Google Scholar
  15. 15.
    Thériault, M., Claramunt, C., Villeneuve, P.Y.: A Spatio-Temporal Taxonomy for the Representation of Spatial Set Behaviours. In: Böhlen, M.H., Jensen, C.S., Scholl, M.O. (eds.) STDBM 1999. LNCS, vol. 1678, pp. 1–18. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  16. 16.
    Tongkumchum, P.: Two-dimensional box plot. Songklanakarin J. Sci. Technol. 27(4), 859–866 (2005)Google Scholar
  17. 17.
    Worton, B.J.: Kernel Methods for Estimating the Utilization Distribution in Home-Range Studies. Ecology 70, 164–168 (1989)CrossRefGoogle Scholar
  18. 18.
    Wickham, H., Stryjewski, L.: 40 Years of Boxplots. Am. Statistician (2011)Google Scholar

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

Personalised recommendations