A Comparative Study Of K-Means And Parallel K-Means Clustering Algorithms For Efficient Data Analysis

Clustering group’s unlabeled data into meaningful patterns. K-Means, a popular partition-based algorithm, offers simplicity and efficiency but struggles with large-scale, high-dimensional data due to scalability and initialization issues. Parallel K-Means addresses these limitations by utilizing parallel and distributed computing frameworks, enhancing performance, scalability, and computational efficiency in clustering tasks. This paper presents a comparative study of traditional K-Means and Parallel K-Means clustering algorithms. It reviews clustering techniques and algorithms, analyzes their methodologies, advantages, and limitations, and highlights the importance of parallel approaches. The study concludes by emphasizing parallelism’s role in enhancing clustering efficiency and scalability for data-intensive applications.