扩展基于相关系数的TOPSIS技术用于区间值的毕达哥拉斯模糊软集:提取,转换和负载技术的案例研究
Rana Muhammad Zulqarnain1, Imran Siddique2, Muhammad Asif3
1School of Mathematical Sciences, Zhejiang Normal University, Jinhua, Zhejiang, China.
PloS one
|October 30, 2023
概括
本研究为区间值的毕达哥拉斯模糊软集 (IVPFSS) 引入了新的相关度,以提高复杂场景中的决策能力. 开发的方法优化了用于商业智能 (BI) 的提取,转换和加载 (ETL) 软件选择.
科学领域:
- 统计 统计 统计 统计
- 决策科学 决策科学 决策科学
- 模糊的集合理论 模糊的集合理论
背景情况:
- 相关性分析对于理解变量关系至关重要.
- 间隔值的毕达哥拉斯模糊软集 (IVPFSS) 为不确定性提供了一个精细的框架.
- 现有的相关系数没有应用于IVPFSS数据.
研究的目的:
- 为IVPFSS提出新的相关系数 (CC) 和加权相关系数 (WCC) 措施.
- 基于这些措施,开发一个多属性决策 (MADM) 模型.
- 为了证明在优化提取,转换和负载 (ETL) 软件选择商业智能 (BI) 的建议方法的应用.
主要方法:
- 对IVPFSS的相关系数 (CC) 和加权相关系数 (WCC) 的发展.
- 使用与理想解决方案相似的订单偏好技术 (TOPSIS) 模型构建优先级技术.
- 应用敏感性分析来验证决策过程.
主要成果:
- 拟议的相关性测量和TOPSIS模型为IVPFSS数据提供了可靠的决策结构.
- 该方法有效地优化了针对BI应用程序的ETL软件选择.
- 经验研究和灵敏度分析证实了与现有模型相比,算法的生产力和一致性.
结论:
- 该研究将相关性分析扩展到IVPFSS,增强不确定性下的决策.
- 开发的基于TOPSIS的方法为复杂的MADM问题提供了一个强大的框架.
- 这项研究对不确定的环境中的统计方法和实际决策做出了重大贡献.
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