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Using the k-Means Clustering Algorithm to Classify Features for Choropleth Maps

2014; University of Toronto Press; Volume: 49; Issue: 1 Linguagem: Francês

1911-9925

Mark Polczynski, Michael Połczyński,

Data Management and Algorithms

Common methods for classifying choropleth map features typically form classes based on a single feature attribute. This technical note reviews the use of the k -means clustering algorithm to perform feature classification using multiple feature attributes. The k -means clustering algorithm is described and compared to other common classification methods, and two examples of choropleth maps prepared using k -means clustering are provided. Abstract: Les methodes courantes de classification des entites des cartes choroplethes forment habituellement des classes basees sur un seul attribut d’entite. Cette note technique passe en revue l’utilisation de l’algorithme de classification automatique a k -moyenne pour classer les entites au moyen d’attributs d’entites multiples. L’auteur decrit l’algorithme de classification automatique a k -moyenne, le compare a d’autres methodes de classification courantes et fournit deux exemples de cartes choroplethes preparees par classification automatique a k moyenne.