Ranking judicial branches using clustering algorithm

The performance of judiciary branches is evaluated based on specific indicators determined by the Statistics and Information Technology Center of Judiciary‎. ‎These indicators‎, ‎which are usually documents recorded in court cases‎, ‎have a specific administrative or judicial score for the branch‎, ‎and by calculating the total scores‎, ‎the performance of the branches is evaluated‎. ‎However‎, ‎with the expansion of these indicators‎, ‎ranking and evaluating branch performance has become more complex‎. ‎In this article‎, ‎clustering is used as one of the most important data mining tools to evaluate branch performance‎. ‎By identifying similar branches‎, ‎examining branches‎, ‎and facing upcoming challenges more effectively‎, ‎more effective decisions can be made in the judiciary system‎. ‎Here‎, ‎to organize 19 law branches based on 49 different administrative and judicial indicators‎, ‎the K-means clustering algorithm is applied based on two criteria of Euclidean dissimilarity distance and random forests‎. ‎In addition‎, ‎the Dunn index is used to evaluate clustering‎. ‎The value of this index is calculated as 0.82 by applying the dissimilarity of random forests‎, ‎indicating the successful performance of the algorithm used in determining similar branches.