Description: Unsupervised learning frameworks are structures and methodologies designed to facilitate the machine learning process without the need for labels or external supervision. Unlike supervised learning, where models are trained with labeled data, unsupervised learning focuses on discovering patterns and intrinsic relationships within the data. These frameworks allow algorithms to identify groupings, associations, and underlying structures in complex datasets. Key features of these frameworks include the ability to handle large volumes of unstructured data, flexibility to adapt to different types of data, and the ability to generate valuable insights without human intervention. The relevance of unsupervised learning frameworks lies in their application in various areas, such as customer segmentation, anomaly detection, and exploratory data analysis, where understanding hidden patterns can lead to more informed decisions and more effective strategies.
