本章目录

25 Clustering 875
25.1 Introduction 875
25.1.1 Measuring (dis)similarity 875
25.1.2 Evaluating the output of clustering methods * 876
25.2 Dirichlet process mixture models 879
25.2.1 From finite to infinite mixture models 879
25.2.2 The Dirichlet process 882
25.2.3 Applying Dirichlet processes to mixture modeling 885
25.2.4 Fitting a DP mixture model 886
25.3 Affinity propagation 887
25.4 Spectral clustering 890
25.4.1 Graph Laplacian 891
25.4.2 Normalized graph Laplacian 892
25.4.3 Example 893
25.5 Hierarchical clustering 893
25.5.1 Agglomerative clustering 895
25.5.2 Divisive clustering 898
25.5.3 Choosing the number of clusters 899
25.5.4 Bayesian hierarchical clustering 899
25.6 Clustering datapoints and features 901
25.6.1 Biclustering 903
25.6.2 Multi-view clustering 903

github下载链接：https://github.com/916718212/Machine-Learning-A-Probabilistic-Perspective-.git