The use of cluster analysis in the classification of similarities in variables associated with agricultural greenhouse gases emissions in OECD countries
Keywords:
cluster analysis, k-means method, Ward’s method, greenhouse gases, agriculture emissionsAbstract
The aim of the research was to group members of the Organization for Economic Co-operation and Development (OECD) into homogeneous subsets for similarities of agricultural variables affecting greenhouse gas emissions. Cluster analysis, which is a tool for exploratory data analysis, was used. This method is based on grouping of elements in a relatively homogeneous class. The most popular non-hierarchical clustering method is k-means. The method is based on an initial a priori assumption of input data set to a predetermined number of classes. In order to verify if the number of clusters was assumed properly, results were compared with another method of cluster analysis – a hierarchical method. Ward’s method of classifying on the basis of minimizing the interclass variance was used. Countries qualified for each cluster derived using k-means were identical to those obtained using Ward’s method. Analysis of the results lead to the conclusion that the geographical location of the countries was key to its inclusion in a cluster this was shown clearly in cluster 1 (Finland, Iceland, Norway, Sweden, Canada), cluster 2 (Austria, Czech Republic, Poland, Slovakia, Switzerland) and cluster 4 (Australia, New Zealand). Group 3 is a 15-element set of countries in predominantly highly industrialized regions.References
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