International Research Journal of Education and Technology

              Unsupervised learning plays a critical role in modern data analysis by identifying hidden structures within unlabeled datasets. Among its various techniques, clustering is one of the most widely used methods for uncovering natural groupings and patterns in data without prior knowledge of outcomes. This paper explores the fundamental concepts, methodologies, and real-world applications of clustering within the realm of unsupervised learning. Common clustering algorithms such as K-Means, Hierarchical Clustering, DBSCAN, and Gaussian Mixture Models are reviewed and compared in terms of their strengths, limitations, and suitability for different types of data. The study emphasizes the importance of selecting appropriate clustering methods based on dataset characteristics and desired outcomes. By revealing inherent structures in data, clustering serves as a foundational tool in fields ranging from market segmentation and social network analysis to image recognition and bioinformatics.