The intuitionistic fuzzy InterCriteria analysis (ICrA) is a new method for correlation analysis, which is based on the concepts of index matrices (IMs) and intuitionistic fuzzy sets (IFSs), aiming at detecting of the dependencies between pairs of rating criteria in both clear and uncertain environments. In the present paper, which is an extension of [39], our aim is to extend ICrA to multidimensional ICrA (n-D ICrA) under intuitionistic fuzzy environment for situations where the evaluations of the objects against multidimensional criteria are completely unknown and to show its efficiency through an application in identifying correlations between pairs of criteria when referred to actual data gathered through estimates of a restaurant’s kitchen staff over a three-year period in Bulgaria. We also present a comparative analysis of the correlations between the evaluated criteria of the kitchen staff, on the basis the application of the correlation methods of ICrA, Pearson (PCA), Spearman (SCA) and Kendall (KCA). The four-correlation analysis yielded very similar correlation coefficients, but only the ICrA can be applied to intuitionistic fuzzy evaluations. It is observed that considerable divergence of the ICrA results from those obtained by the other classical correlation analyzes, is only found when the input data contains mistakes.

中文翻译：

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餐厅的多维直觉模糊区间分析

直觉模糊区间分析（ICrA）是一种新的关联分析方法，它基于索引矩阵（IM）和直觉模糊集（IFS）的概念，旨在检测两个清晰的评级标准对之间的依赖性和不确定的环境。本文是对[39]的扩展，我们的目的是将直觉模糊环境下的ICrA扩展为多维ICrA（nD ICrA），用于针对多维标准对对象进行评估的情况完全未知并显示其效率通过应用程序来识别一对标准之间的相关性，这些应用程序是通过参照保加利亚三年来对餐厅厨房工作人员的估算而收集到的实际数据得出的。我们还根据ICrA，Pearson（PCA），Spearman（SCA）和Kendall（KCA）的相关方法的应用，对厨房工作人员评估标准之间的相关性进行了比较分析。四相关分析得出的相关系数非常相似，但是只有ICrA可以应用于直觉模糊评估。可以观察到，只有当输入数据包含错误时，才会发现ICrA与其他经典相关分析所获得的结果有相当大的差异。但只有ICrA可以应用于直觉模糊评估。可以观察到，只有当输入数据包含错误时，才会发现ICrA与其他经典相关分析所获得的结果有相当大的差异。但是只有ICrA可以应用于直觉模糊评估。可以观察到，只有当输入数据包含错误时，才会发现ICrA与其他经典相关分析所获得的结果有相当大的差异。