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

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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