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Erienne, Laurent, 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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State of the Art in Patterns for Point Cluster Analysis_EtienneDevogeleMcArdleICCSA2014.pdf
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Official URL: http://link.springer.com/chapter/10.1007%2F978-3-3...
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: | 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 |
| Related URLs: | |
| URI: | https://mural.maynoothuniversity.ie/id/eprint/6085 |
| 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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