Porovnání shlukovacích algoritmů v nástroji WEKA

Abstract

This thesis deals with algorithms for clustering data. Their performance and testing for the collection of data from social networks using WEKA tool. The aim of this thesis is to compare these algorithms based on the use of CPU load, memory usage and value of computing time required for clustering. In this work it was found out that the three experimental parameters for each monitored algorithms are significantly different.