The Comparative Effects of Clustering Algorithms on CPU and GPU
Pınar Ersoy, Mustafa Erşahin, Buket ERŞAHİN · DergiPark (Istanbul University) · 2022
The algorithm clustering can be defined as the operation of separating the populace or pieces of information into various groups. This article aims to construct a performance comparison for Partitional Clustering by using random, k-means++ algorithms implemented with Scikit-Learn and k-means++, Tunnel k-means algorithms implemented with TensorFlow-GPU by means of their execution times. As a final output, a related comparison table will be printed by supplying their framework specifications. Since the article does not focus on the context of data, the necessary data sets will be produced in a random manner.