Spam emails continue to pose significant challenges to users and organizations, necessitating robust classification techniques for effective filtering. This paper presents an overview of spam classification methods leveraging unsupervised learning algorithms. By eschewing the need for labeled data, unsupervised learning offers a promising avenue for identifying and categorizing spam without explicit human intervention. The abstract discusses the key concepts and methodologies employed in unsupervised spam classification, encompassing clustering algorithms such as K-means, hierarchical clustering, and density-based methods.
