This chapter introduces a major unsupervised learning topic: clustering. To ensure a good transition from supervised learning to unsupervised learning, we will first try to fill some possible knowledge gaps through a review of the basics of unsupervised learning. This includes changes from supervised to unsupervised learning, a framework of unsupervised learning, and an overview of clustering algorithms. Based on them, details will be provided for K-means, DBScan, GMM, and Agglomerative Hierarchical as the most representative algorithms for four main categories of clustering algorithms: centroid-based, density based, distribution based, and connectivity based, respectively. Finally, the evaluation of clustering, including popular metrics and their use, will be discussed.