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Improved Automatic Clustering Using a Multi-Objective Evolutionary Algorithm With New Validity measure and application to Credit Scoring | ||
Iranian Journal of Optimization | ||
مقالات آماده انتشار، پذیرفته شده ، انتشار آنلاین از تاریخ 31 شهریور 1396 | ||
نوع مقاله: Research Paper | ||
نویسندگان | ||
majid mohammadi rad ![]() | ||
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 | ||
چکیده | ||
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. | ||
کلیدواژهها | ||
Clustering؛ Data Mining؛ Evolution Algorithm؛ Credit Score, Clustering Validity Measure | ||
آمار تعداد مشاهده مقاله: 246 |
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