Comparison of modern clustering algorithms for twodimensional data

Abstract

Cluster analysis or clustering is a task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is the main task of exploratory data mining and a common technique for statistical data analysis used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics.The topic of this paper is modern methods of clustering. The paper describes the theory needed to understand the principle of clustering and descriptions of algorithms used with clustering, followed by a comparison of the chosen methods. Proceedings 28th European Conference on Modelling and Simulation © ECMS Flaminio Squazzoni, Fabio Baronio, Claudia Archetti, Marco Castellani (Editors).