The Comparative Effects of Clustering Algorithms on CPU and GPU

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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