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    State of the Art in Patterns for Point Cluster Analysis


    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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    Official URL: http://link.springer.com/chapter/10.1007%2F978-3-3...


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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:

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