Unsupervised Learning: Techniques, Applications, and Testing Methodologies

Finding hidden patterns in data to reveal meaningful relationships is one of the major uses of unsupervised learning (UL). With the increasing volumes of raw, unlabeled data across applications such as anomaly detection, fraud detection, recommender systems, genetic research, etc., UL techniques have become widely used for discovering meaningful features in datasets without supervision. Various UL algorithms including clustering algorithms, Apriori algorithm, autoencoders, etc., support these applications, each with their own strengths and limitations. It is important to evaluate UL algorithms to ensure the reliability and accuracy of their outcomes. In this paper, we aim to address three key questions: (i) Which domains have successfully applied UL techniques? (ii) What are the strengths and limitations of UL algorithms? (iii) What testing methodologies exist for UL?