mohammadi rad, M., afzali, M. (2017). Improved Automatic Clustering Using a Multi-Objective Evolutionary Algorithm With New Validity measure and application to Credit Scoring. Iranian Journal of Optimization, (), -.
majid mohammadi rad; mahdi afzali. "Improved Automatic Clustering Using a Multi-Objective Evolutionary Algorithm With New Validity measure and application to Credit Scoring". Iranian Journal of Optimization, , , 2017, -.
mohammadi rad, M., afzali, M. (2017). 'Improved Automatic Clustering Using a Multi-Objective Evolutionary Algorithm With New Validity measure and application to Credit Scoring', Iranian Journal of Optimization, (), pp. -.
mohammadi rad, M., afzali, M. Improved Automatic Clustering Using a Multi-Objective Evolutionary Algorithm With New Validity measure and application to Credit Scoring. Iranian Journal of Optimization, 2017; (): -.
Improved Automatic Clustering Using a Multi-Objective Evolutionary Algorithm With New Validity measure and application to Credit Scoring
Articles in Press, Accepted Manuscript , Available Online from 22 September 2017
1Department of Computer and Information Technology, Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2Faculty of Computer Engineering, Islamic Azad University, Zanjan Branch, Zanjan,Iran
Receive Date: 29 June 2017,
Revise Date: 30 August 2017,
Accept Date: 22 September 2017
Abstract
In data mining, clustering is one of the important issues for separation and classification with groups like unsupervised data. In this paper, an attempt has been made to improve and optimize the application of clustering heuristic methods such as Genetic, PSO algorithm, Artificial bee colony algorithm, Harmony Search algorithm and Differential Evolution on the unlabeled data of an Iranian bank with the credit scoring approach. A survey was also used to measure the clustering validity index which resulted in a new validity index. Finally, the results were compared to identify the best algorithm and validity measure.
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