Unsupervised learning algorithms do not use labeled datasets. They create models that use intrinsic relations in data to find hidden patterns that they can use for making predictions. The most well-known unsupervised learning technique is clustering. Clustering involves dividing a given set of data in a limited number of groups according to some intrinsic properties of data items. Clustering is applied in market researches, different types of exploratory analysis, deoxyribonucleic acid (DNA) analysis, image segmentation, and object detection. Typical algorithms for creating models for performing clustering are k-means, k-medoids, Gaussian mixture models, hierarchical clustering, and hidden Markov models. Some of these algorithms are explained in the following chapters of this book.