Using K-Means and K-Medoids Methods for Multivariate Mapping

Year 2016, Special Issue (2016), 342 - 345, 01.12.2016

Hüseyin Zahit Selvi , Burak Çağlar

https://doi.org/10.18100/ijamec.274494

Cited By: 1

Abstract

Multivariate mapping is the visual exploration of multiple attributes
using a map or data reduction technique. The simultaneous display of sometimes
multiple features and their respective multivariate attributes allows for
estimation of the degree or spatial pattern of cross-correlation between
attributes. Multivariate mapping integrates computational, visual, and
cartographic methods to develop a visual approach for exploring and
understanding spatiotemporal and multivariate patterns. More than one attribute
can be visually explored and symbolized using numerous statistical
classification systems or data reduction techniques. In this sense, clustering
analysis methods can be used for multivariate mapping. k-means and k-medoids
methods which are non-hierarchical clustering analysis methods were analyzed in
this study. The aim of this study is to determine the success of the spatial
analysis of the multivariate maps produced by these methods. For this aim,
classes and multivariate maps created with these methods from traffic accident
data of two different years in Turkey were presented. In addition usability of
such maps in risk management and planning was discussed.  

Keywords

Multivariate mapping, Data mining, Cluster analysis, Visualization

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