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 ¹؛ mahdi afzali²

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.

کلیدواژه ها

Clustering؛ Data Mining؛ Evolution Algorithm؛ Credit Score, Clustering Validity Measure