Cognitive computing has deep extents, which embrace different features of cognition. In the decision-making process, multi-criteria decision making is credited as a cognitive-based human action. However, to treat and unite the information from several resources, the most vital stage is data collection. Intuitionistic fuzzy set (IFS) is one of the most robust and trustworthy tools to accomplish the imprecise information with the help of the membership degrees. In addition to this, an information measure plays an essential role in treating uncertain information to reach the final decision based on the degree of the separation between the pairs of the numbers. Motivated by these, this paper aims to present the novel information measures using four different centers namely centroid, orthocenter, circumcenter and incenter under the IFS environment to address the cognitive-based human decision-making problems. The present work is divided into three folds. The first fold is to propose a technique of transforming intuitionistic fuzzy values into general triangular fuzzy numbers (TFNs). The right-angled and isosceles TFNs are special cases of the proposed transformation technique. The second fold is to develop distance and similarity measures using four different centers namely centroid, orthocenter, circumcenter and incenter of transformed TFNs. The basic axioms of the proposed measures are investigated in detail. The third fold is to justify superiority and validity of the proposed measures. The effectiveness of the developed measures is examined by applying it in clustering as well as the pattern recognition problems, and their results are correlated with some prevailing studies. Additionally, a clustering technique is discussed based on the stated measures to classify the objects. A detailed comparative analysis is done with some of the existing measures and concludes that several existing measures fail to discriminate the results under the different instances such as division by zero problems or counter-intuitive cases while the proposed measure has successfully overcome this drawback.

认知计算具有广泛的领域，涵盖了认知的不同特征。在决策过程中，多准则决策被认为是基于认知的人类行为。但是，要处理和整合来自多种资源的信息，最关键的阶段是数据收集。直觉模糊集（IFS）是在隶属度的帮助下完成不精确信息的最健壮和可信赖的工具之一。除此之外，信息量在处理不确定信息以基于数字对之间的分隔程度做出最终决定方面起着至关重要的作用。基于这些动机，本文旨在通过四个不同的中心（质心，正交中心，在IFS环境下的中心和内心以解决基于认知的人类决策问题。本作品分为三部分。第一步是提出一种将直觉模糊值转换为通用三角模糊数（TFNs）的技术。直角和等腰TFN是所提出的转换技术的特例。第二折是使用四个不同的中心（即转换的TFN的质心，正交中心，外心和内心）来开发距离和相似性度量。拟议措施的基本公理进行了详细研究。第三方面是证明所提议措施的优越性和有效性。通过将其应用到聚类以及模式识别问题中来检验已开发措施的有效性，他们的结果与一些流行的研究相关。另外，基于陈述的措施对聚类技术进行了讨论，以对对象进行分类。对一些现有措施进行了详细的比较分析，得出的结论是，几种现有措施未能区分不同情况下的结果，例如除以零问题或违反直觉的情况，而所提出的措施已成功克服了这一缺点。