Statistical and fuzzy clustering methods and their application to clustering provinces of Iraq based on agricultural products

Document Type : Original Article


1 Faculty of Mathematics and Computer Science, Amirkabir University of Technology, Tehran

2 School of Engineering Science, College of Engineering, University of Tehran


The important approaches to statistical and fuzzy clustering are reviewed and compared, and their applications to an agricultural problem based on a real-world data are investigated. The methods employed in this study includes some hierarchical clustering and non-hierarchical clustering methods and Fuzzy C-Means method. As a case study, these methods are then applied to cluster 15 provinces of Iraq based on some agricultural crops. Finally, a comparative and evaluation study of different statistical and fuzzy clustering methods is performed. The obtained results showed that, based on the Silhouette criterion and Xie-Beni index, fuzzy c-means method is the best one among all reviewed methods


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