An Introduction to Deep Clustering

Abstract

Driven by the explosive growth in available data and decreasing costs of computation, Deep Learning (DL) has found much of its fame in problems involving classification tasks which are considered supervised learning. Deep learning has also been widely used to learn richer and better data representations from big data, without relying too much on human engineered features. Even though it started mostly within the realm of supervised learning, deep learning's success has recently inspired several deep learning-based developments in clustering algorithms which sit squarely within unsupervised learning. Most DL-based clustering approaches result in both deep representations and (either as an explicit aim or as a byproduct) clustering outputs, hence we refer to all these approaches as Deep Clustering. In this chapter, we present a simplified taxonomy of Deep Clustering methods, based mainly on the overall procedural structure or design which helps beginning readers quickly grasp how almost all approaches are designed, while allowing more advanced readers to learn how to design increasingly sophisticated deep clustering pipelines that fit their own machine learning problem-solving aims. Like DL, Deep Clustering promises to leave an impact on diverse application domains ranging from computer vision and speech recognition to recommender systems and natural language processing.