Title: Clustering Meets Deep Learning: Evolution of Unsupervised Representation Learning
Authors: Virendra Tank
Volume: 10
Issue: 6
Pages: 20-27
Publication Date: 2026/06/28
Abstract:
The integration of deep learning with clustering has revolutionized unsupervised representation learning, enabling the discovery of complex patterns in high-dimensional data without labeled supervision. This review examines the evolution from traditional clustering methods to modern deep clustering architectures, including Deep Embedded Clustering (DEC), Joint Unsupervised Learning (JULE), and DeepCluster. We explore contrastive learning approaches that have achieved remarkable success in learning meaningful representations, graph neural networks for clustering structured data, and evaluation metrics tailored for deep clustering. Through comprehensive comparison with traditional methods and analysis of applications in image segmentation, document clustering, and customer segmentation, we highlight the transformative impact of deep learning on unsupervised learning paradigms and identify future research directions.