Unsupervised Learning - A Systematic Literature Review

Abstract

Machine learning (ML) is a data-driven approach in which machines learn from the data without the involvement ofhumans. Several domains take advantage of mind-boggling applications of ML. There are three main learning problems inML: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves the trainingof the model on a labelled dataset. Unsupervised learning involves the training of a model in an unlabeled dataset. Themodel learns on its own by learning the features of the training dataset. Based on that learning features, the model makespredictions on test data. Several unsupervised learning approaches and algorithms range from clustering, k-means toagglomerative, Principal component analysis, and Fuzzy C-means. Clustering involves the grouping of objects based ontheir similar features. The algorithms in clustering are categorized into two broad categories such as hierarchal clusteringand partitional clustering.