An Unsupervised Machine Learning Algorithms: Comprehensive Review

Abstract

Machine learning (ML) is a data-driven strategy in which computers learn from data without human intervention. The outstanding ML applications are used in a variety of areas. In ML, there are three types of learning problems: Supervised, Unsupervised, and Semi-Supervised Learning. Examples of unsupervised learning techniques and algorithms include Apriori algorithm, ECLAT algorithm, frequent pattern growth algorithm, clustering using k-means, principal components analysis. Objects are grouped based on their same properties. The clustering algorithms are divided into two categories: hierarchical clustering and partition clustering. Many unsupervised learning techniques and algorithms have been created during the last decade, and some of them are well-known and commonly used unsupervised learning algorithms. Unsupervised learning approaches have seen a lot of success in disciplines including machine vision, speech recognition, the creation of self-driving cars, and natural language processing. Unsupervised learning eliminates the requirement for labeled data and human feature engineering, making standard machine learning approaches more flexible and automated. Unsupervised learning is the topic of this survey report.