Enhancing the Performance of Unsupervised Machine Learning using Parallel Computing: A Comparative Analysis

The increasing popularity of unsupervised machine learning techniques, particularly in clustering algorithms, is evident due to their ability to efficiently generate clusters from large datasets. As data volumes continue to expand, traditional methods become less feasible, prompting the exploration of parallel computing solutions for enhanced performance. This paper assesses the efficacy of parallel computing, focusing on Fuzzy C-Means clustering. Three implementations are compared: Sequential, Parallel using MPI, and Parallel using the Cloud. The adoption of parallel computing significantly improves scalability, leading to a 50% reduction in processing time and a 30% enhancement in overall system performance.