Modeling and simulation of a K-means clustering algorithm in MATLAB and Python

Abstract

This paper presents an analysis of the K-means algorithm, a popular unsupervised learning technique used for clustering data based on similar features. For this, the implementation and performance of the K-means algorithm were compared in the Python and MATLAB programming languages. In addition, modeling and simulation of the algorithm were performed to compare the computation time and clustering quality in the two languages. The experimental results demonstrate that the K-means algorithm is a powerful tool for data clustering and can be used effectively in various applications. Although both Python and MATLAB are languages capable of efficiently implementing the K-means algorithm, MATLAB resulted in a shorter computation time.