Optimization Inspired on Herd Immunity Applied to Non-Hierarchical Grouping of Objects

Abstract

Characterized as one of the most important operations related to data analysis, one non-hierarchical grouping consists of, even without having any information about the elements to be classified, establish upon a finite collection of objects, the partitioning of the items that constitute it into subsets or groups without intersecting, so that the elements that are part of a certain group are more similar to each other than the items that belong to distinct group. In this context, this study proposes the application of a meta-heuristic inspired by herd immunity to the determination of the non-hierarchical grouping of objects, and compares the results obtained by this method with the answers provided by four other grouping strategies, described in the literature. In particular, the resulting arrangements of the classification of 33 benchmark collections, performed by the suggested algorithm, by the metaheuristic inspired by the particle swarm, by the genetic algorithm, by the K-means algorithm and by the meta-heuristic inspired by the thermal annealing process, were compared under the perspective of 10 different evaluation measures, indicating that the partitions established by the meta-heuristic inspired by the herd immunity may, in certain respects, be more favorable than the classifications obtained by the other clustering methods.