Clustering and Unsupervised Learning

Abstract

Machine learning (ML) is an approach driven by data, wherein computers acquire knowledge from information without requiring human interference. Artificial intelligence (AI) and machine learning (ML) have made significant contributions across diverse research domains, leading to enhanced outcomes. Clustering is defined as a fundamental challenge in various data-driven fields, representing an unsupervised learning model. Unsupervised learning methods and algorithms encompass the Apriori algorithm, ECLAT algorithm, frequent pattern growth algorithm, k-means clustering, and principal components analysis. Unsupervised learning methods have achieved notable success in fields such as machine vision, speech recognition, the development of autonomous vehicles, and natural language processing. This chapter provides a brief explanation of unsupervised clustering approaches. It also discusses literature review, intriguing challenges, and future prospects in the realm of unsupervised deep clustering.