The Comparative Effects of Clustering Algorithms on CPU and GPU

Year 2022, Volume: 2 Issue: 2, 19 - 27, 01.10.2022

Pınar Ersoy , Mustafa Erşahin , Buket Erşahin

Abstract

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.

Keywords

clustering, random k-means, k-means++, tunnel k-means, algorithm performance, TensorFlow-GPU, Scikit-Learn

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