Erienne, Laurent and Devogele, Thomas and McArdle, Gavin (2014) State of the Art in Patterns for Point Cluster Analysis. Lecture Notes in Computer Science - Computational Science and Its Applications – ICCSA 2014, 8579. pp. 252-266. ISSN 0302-9743

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Abstract

Nowadays, an abundance of sensors are used to collect very large da-tasets containing spatial points which can be mined and analyzed to extract mean-ingful patterns. In this article, we focus on different techniques used to summa-rize and visualize 2D point clusters and discuss their relative strengths. This arti-cle 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.

Item Type:	Article
Keywords:	State of the Art; Patterns; Point Cluster Analysis;
Academic Unit:	Faculty of Science and Engineering > Research Institutes > National Centre for Geocomputation, NCG
Item ID:	6085
Identification Number:	https://doi.org/10.1007/978-3-319-09144-0_18
Depositing User:	Dr. Gavin McArdle
Date Deposited:	06 May 2015 10:58
Journal or Publication Title:	Lecture Notes in Computer Science - Computational Science and Its Applications – ICCSA 2014
Publisher:	Springer Verlag
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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