Clustering, or cluster analysis, is a task that belongs to unsupervised learning paradigm. This chapter starts from the clustering problem and definition and studies the working principle employing formal and illustrated descriptions. Second, we discuss the relevant elements such as hard and soft, as well as linear and nonlinear clustering. We next divide clustering algorithms into classical and neo-classical methods and discuss them respectively. Then, we introduce several typical clustering algorithms, including k-means, Gaussian mixture clustering, DBSCAN, and the density peak clustering. After that, we explain evaluation metrics including adjusted Rand index (ARI), adjusted mutual information (AMI), Fowlkes-Mallows index (FMI), V-measure, Calinski-Harabasz index (CHI), Davies-Bouldin index (DBI), and silhouette coefficient (SC). Finally, the application fields of clustering are briefly introduced.