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.
Farhadi, Z., & Farzammehr, M. A. (2024). Ranking judicial branches using clustering algorithm. Journal of Statistical Modelling: Theory and Applications, 5(1), 53-64. doi: 10.22034/jsmta.2024.21327.1133
MLA
Zohreh Farhadi; Mohadeseh Alsadat Farzammehr. "Ranking judicial branches using clustering algorithm", Journal of Statistical Modelling: Theory and Applications, 5, 1, 2024, 53-64. doi: 10.22034/jsmta.2024.21327.1133
HARVARD
Farhadi, Z., Farzammehr, M. A. (2024). 'Ranking judicial branches using clustering algorithm', Journal of Statistical Modelling: Theory and Applications, 5(1), pp. 53-64. doi: 10.22034/jsmta.2024.21327.1133
VANCOUVER
Farhadi, Z., Farzammehr, M. A. Ranking judicial branches using clustering algorithm. Journal of Statistical Modelling: Theory and Applications, 2024; 5(1): 53-64. doi: 10.22034/jsmta.2024.21327.1133
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