Data Clustering Using Environmental Adaptation Method

Extracting useful information from a large-scale dataset and transforming it into the required structure for further use is termed data mining. One of the most important techniques of data mining is data clustering that is responsible for grouping the data into meaningful groups (clusters). Environmental Adaptation Method (EAM) is an optimization algorithm that has already proved its efficacy in solving global optimization problems. In this paper, an approach based on a new version of EAM has been suggested for solving the data clustering problem. To validate the utility of the suggested approach, four recently developed metaheuristics have been implemented and compared for six standard benchmark datasets. Various comparative performance analysis based on experimental values have justified the competitiveness and effectiveness of the suggested approach.