New Data Mining approach for clustering Export Credit Agencies (ECAs) based on performance criteria: a bibliometric citation analysis for the period 2005 to 2020

IF 0.5 Q3 MATHEMATICS
Seyed Arash Shahraeini, S. Tabrizi, C. Spulbar, Ramona Birau, Amir Karbassi Yazdi
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引用次数: 0

Abstract

Nowadays, ECAs have a crucial role in the export of production, creating job opportunities for countries and growth of economic indicators.This research aims first to estimate the performance of ECAs based on covering all countries of the world and ranking the countries based on the issue of export credit according to their performance and clustering techniques. For evaluation performance of these ECAs, clustering techniques are used to put them in the categories according to their performance between 2005 to 2020 in the fourth quarter. The context of clustering shows the rank of each cluster, and then exporters can choose a better choice from them. Moreover, for reinsurance,other ECAs can find out which ECAs have high performance. The result indicates that ranking the ECAs and show the performance of each cluster.
基于绩效标准的出口信贷机构聚类的新数据挖掘方法:2005 - 2020年文献计量引用分析
如今,eca在产品出口、为各国创造就业机会和经济指标增长方面发挥着至关重要的作用。本研究首先在覆盖全球所有国家的基础上对信用评级机构的绩效进行估计,并根据信用评级机构的绩效和聚类技术对出口信贷发行国家进行排名。为了评估这些eca的绩效,我们使用聚类技术将它们根据2005年至2020年第四季度的绩效进行分类。聚类上下文显示每个聚类的排名,然后导出者可以从中选择一个更好的选择。此外,对于再保险,其他信用担保机构可以发现哪些信用担保机构的绩效较高。结果表明,可以对eca进行排序,并显示每个集群的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
1.10
自引率
10.00%
发文量
18
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