Significant business implications and effective handling of supply side exceptions require a successful Supplier Base Management (SBM). The process of clustering manages the number of suppliers by grouping them on the basis of similar characteristics that reduces the number of variables impacting the operations. Several existing categorical clustering techniques for such grouping contributed well than their predecessors however, the accuracy, uncertainty, entropy and computation are key measures need to be improved. Especially, the existing clustering techniques cluster randomly in case of independent and insignificant type of data. The aim of this research is to introduce a novel rough value set based categorical clustering technique named Maximum Value Attribute (MVA). The proposed MVA techniques overcome the issues of existing techniques by combining the concept of Number of Automated Clusters (NoACs) with rough value set which makes it novel and significant clustering idea. Few relevant and necessary propositions are illustrated to prove the effectiveness of NoACs. The existing and proposed rough sets based and classical categorical clustering techniques are compared in terms of purity, entropy, accuracy, rough accuracy, time and iterations. Experimental results based on a SBM and fifteen (15) benchmark data sets reveal better performance of MVA. The experimental results show significant overall percentage improvement of proposed MVA technique against existing rough based techniques for iterations (99.7%), time (99.4%), number of obtained clusters (84%), purity (32%), entropy (32%), accuracy (20%), and rough accuracy (13%).

重大的业务影响和对供应方异常的有效处理要求成功的供应商基础管理（SBM）。集群过程通过根据相似的特征对供应商进行分组来管理供应商的数量，从而减少了影响运营的变量数量。几种现有的用于此类分组的分类聚类技术比其前任做出了很大贡献，但是，准确性，不确定性，熵和计算是需要改进的关键指标。特别是，在数据类型独立且无关紧要的情况下，现有的聚类技术会随机聚类。这项研究的目的是介绍一种新的基于粗糙值集的分类聚类技术，称为最大值属性（MVA）。提出的MVA技术通过将自动集群数（NoAC）的概念与粗糙值集相结合而克服了现有技术的问题，这使其成为新颖而有意义的集群思想。很少有相关和必要的提议被证明来证明NoAC的有效性。在纯度，熵，准确性，粗略准确性，时间和迭代方面，比较了现有和建议的基于粗糙集的分类集和经典分类聚类技术。基于SBM和十五（15）个基准数据集的实验结果表明，MVA具有更好的性能。实验结果表明，与现有的基于粗糙技术的迭代（99.7％），时间（99.4％），获得的簇数（84％），纯度（32％），熵（32％）相比，提出的MVA技术相对于现有的基于粗糙技术的总体百分比显着提高，准确度（20％），