Outline 历些毛子代拔大》 XIDIAN UNIVERSITY ▣Background What can be clustered? Problems in K-XXX(Means/Medoid/Center...) ■Similarity Measure Basics,not ■Convex and Concave state-of-the-art Problems in Gaussian Mixture Model Problems in Matrix Factorization Multinomial and Sparsity Keywords:Clustering,K-Means/Medoid,Similarity Computation,GMM,MF, Multinomial Distribution 2017/4/13 Software Engineering 2017/4/13 Software Engineering Outline  Background  What can be clustered?  Problems in K-XXX (Means/Medoid/Center…)  Similarity Measure  Convex and Concave  Problems in Gaussian Mixture Model  Problems in Matrix Factorization  Multinomial and Sparsity 2 Keywords: Clustering, K-Means/Medoid, Similarity Computation, GMM, MF, Multinomial Distribution Basics, not state-of-the-art