Data Clustering in the Age of Big Data: Challenges, Methodologies and Analytical Implications

Abstract

With the exponential proliferation of data, Big Data analysis has become a cornerstone of modern decision-making. At the heart of this analysis lies clustering, an unsupervised learning technique aimed at grouping similar objects. This article explores the crucial role of clustering in the Big Data analytical process, examining the specific challenges posed by the volume, velocity, variety and veracity of data. We review traditional clustering methodologies and their adaptations, as well as algorithms specifically designed for Big Data environments. Practical implications, use cases and future prospects, including integration with deep learning and distributed systems, are also discussed. The aim is to provide an in-depth understanding of how clustering can unlock valuable insights from massive and complex datasets.

Keywords: Data clustering, Big Data, Data analysis, Unsupervised learning, Clustering algorithms, Big Data challenges, Data mining, Artificial intelligence.

Received Date: February 22, 2026

Accepted Date: March 14, 2026

Published Date: April 02, 2026

Available Online at: https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/619