Unsupervised Learning for Complex Data Clustering: Methods and Applications

Abstract

Unsupervised learning, a fundamental area of machine learning, plays a pivotal role in clustering complex data without labeled outputs. Clustering, one of the most prominent applications of unsupervised learning, helps identify patterns, groupings, and structures within unlabeled datasets. This paper delves into the methodologies behind unsupervised clustering algorithms and their applications across various domains. We explore traditional clustering techniques, including K-means, hierarchical clustering, and DBSCAN, as well as recent advances such as deep learning-based clustering methods. Furthermore, we discuss the challenges of applying unsupervised learning to real-world data, such as high dimensionality, scalability, and interpretability, and how current methods address these issues. The paper also highlights emerging trends in unsupervised learning, such as the integration of unsupervised and supervised learning models and the use of generative models for data clustering.