Comparative Study of Clustering Algorithms in Parallel and Serial Environments

Abstract

Machine learning has become a core part of computing and has affected countless sectors with better implementations of existing systems. Machine learning algorithms use various methods to organize and learn from data and Clustering is one such method. Clustering as the name suggests, forms different clusters of data from the dataset based on the characteristics. However, clustering datasets could be onerous and might become worse when the number of clusters or if the number of data points is increased. Parallelizing the algorithms is one way by which the time taken can be reduced. Clustering algorithms can be parallelized by optimizing the algorithm to make use of multiple CPUs or multiple cores of a single CPU by sharing the workload. This paper focuses on the performance analysis of parallelized clustering algorithms and other mainstream clustering algorithms. DBSCAN (Density-Based Spatial Clustering of Applications with Noise), K-Means, Mini-Batch K-Means, Mean Shift are the chosen algorithms from different types of clustering to diversify the comparison. This paper will provide a comparative analysis of the performance between the different clustering algorithms by controlling the environment to either be single or multi-threaded.