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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Becketti, S., Gould, W.: Rangefinder Box Plots, A Note, The American Statistician (1987)
Berkhin, P.: A survey of clustering data mining techniques. In: Grouping Multidimensional Data, pp. 25–71. Springer, Heidelberg (2006)
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)
Getz, W.M., Wilmers, C.C.: A local nearest-neighbor convex-hull construction of home ranges and utilization distributions. Ecography 27, 489–505 (2004)
Goldberg, K., Iglewicz, B.: Bivariate Extensions of the Box Plot. American Statistician 34(3), 307–320 (1992)
Jain, A., Murty, M., Flynn, P.: Data clustering: a review. ACM Computing Surveys (CSUR) 31(3), 264–323 (1999)
Kenward, R.: Wildlife radio tagging. Academic Press, Inc., London (1987)
Lefever, D.W.: Measuring Geographic Concentration by Means of the Standard Deviational Ellipse. American Journal of Sociology 32(1), 88–94 (1926)
Mohr, C.O.: Table of equivalent populations of North American small mammals. American Midland Naturalist 37(1), 223–249 (1947)
Pearson, K.: On Lines and Planes of Closest Fit to Systems of Points in Space. Philosophical Magazine 2(11), 559–572 (1901)
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)
Rousseeuw, P., Ruts, I., Tukey, J.: The bagplot: a bivariate boxplot. The American Statistician 53(4), 382–387 (1999)
Small, C.A.: Survey of Multidimensional Medians. International Statistical Review 58(3), 263–277 (1990)
Tukey, J.: Exploratory Data Analysis. Addison-Wesley (1977)
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)
Tongkumchum, P.: Two-dimensional box plot. Songklanakarin J. Sci. Technol. 27(4), 859–866 (2005)
Worton, B.J.: Kernel Methods for Estimating the Utilization Distribution in Home-Range Studies. Ecology 70, 164–168 (1989)
Wickham, H., Stryjewski, L.: 40 Years of Boxplots. Am. Statistician (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Etienne, L., Devogele, T., McArdle, G. (2014). State of the Art in Patterns for Point Cluster Analysis. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol 8579. Springer, Cham. https://doi.org/10.1007/978-3-319-09144-0_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-09144-0_18
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09143-3
Online ISBN: 978-3-319-09144-0
eBook Packages: Computer ScienceComputer Science (R0)