Clustering with simulated annealing and genetic algorithm

Abstract

The article focuses on clustering algorithms. Clustering is a data mining algorithm that groups input data based on their related similarities. In the research, however, the clustering was not solved by the usual classical method, but by metaheuristics. From the metaheuristics, the simulated annealing and the genetic algorithm were selected. In this article, I present test results for three data sets, based on which it can be concluded that the algorithms find the cluster boundaries efficiently.