Skip to Main Content
In this chapter, a general review of Unsupervised Learning is conducted. Generic clustering issues are first defined and explained. A survey of traditional approaches to Unsupervised Learning is then presented, and the chapter concludes in with a discussion of assessment measures and limitations in the evaluation of clustering solutions. It presents a brief survey of the issues that need to be considered in assessing the validity of unsupervised clustering results. Distance metrics, cluster quality, and cluster validity are each vast topics unto themselves and become essential, yet difficult considerations in the evaluation of a clustering solution within unsupervised contexts. These are expanded upon in the chapter. It outlines some of the more popular clustering approaches from the literature namely, Iterative Mean-Squared Error Approaches, Mixture Decomposition Approaches, Agglomerative Hierarchical Approaches, Graph-Theoretic Approaches, Evolutionary Approaches and Neural Network Approaches.