A Knowledge Discovery of Relationships among Dataset Entities Using Optimum Hierarchical Clustering by DE Algorithm

In recent years, discovering relationships among entities and their features in a dataset has been received a great attention in data analytics. This study aims to reveal the relationships among entities in a dataset according to a specific sequence of features which are guided according to the accuracy of the hierarchical clustering made up by the features. In this paper, a new metric, called Discriminating Features based Cohesion (DFC) factor, is defined as pair-wise stickiness measure among entities which indicates their degree of attachment (i.e., cohesive force). In this direction, a new framework is proposed; which utilizes an evolutionary algorithm (i.e., DE) for the optimal discriminating feature selection and also a hierarchical clustering method for computing DFC factors. DE algorithm is employed to identify features which their clustering hierarchical tree has the maximum accuracy, then the intermediate and final DFC factors' matrices are computed by using a hierarchical clustering of the most discriminating features. The intermediate and final DFC factors' matrices have been utilized to discovery the knowledge among Dataset Entities including answering crucial data mining queries which cannot be answered by using a standalone clustering method. In order to conduct a case study, a real-world dataset is utilized; which contains 17 entities (i.e., countries) presented by corresponding 24 continuous features. The DE algorithm finds the most discriminating features in each step, which are eliminated for the next step to calculate a matrix of DFC factors. In the final step, the proposed method ranks the entities in terms of their DFC factor and features based on their elimination order (i.e., discrimination power).