模式识别聚类算法和线性判别算法.ppt

Pattern Classification;Male-Female;Male-Female;;1.2 Hierarchical clustering; 1.3 Function;C-means clustering：;Simulation Result of C-means clustering ;Simulation Result of C-means clustering ;;Simulation Result of Hierarchical clustering;Simulation Result of Hierarchical clustering;1.5 Effect of initial value of different clustering on clustering results;;2.1 Linear decision ;2.1 Linear decision ; Source Program of RandQ ;Source Program of  Linear decision ;Simulation Result of Linear decision ;Simulation Result of Linear decision ;Program description: Pdist function is used to calculate the distance between each?other, and then the linkage function is used to establish the hierarchical structure tree. By comparing the classification results, the least error algorithm is selected for the class of inner square distance. The final call cluster function, the structure of the tree to cluster, determine the final category. ;2.2 Hierarchical clustering; 2.linkage Z=linkage(Y,‘method’) Input value Description: Y for the return of the pdist function M* (M-1) /2 elements of the row vector, using the method parameter specified algorithm to calculate the system clustering tree. Method: can be valued as follows : ‘single’：最短距离法（默认）； ‘complete’：最长距离法； ‘average’：未加权平均距离法； ‘weighted’： 加权平均法； ‘centroid’：质心距离法； median’：加权质心距离法； ‘ward’：内平方距离法（最小方差算法）;3.cluster T=cluster(Z,…) Description: according to the output of linkage function Z to create classification.;load feature_table; W=feature_table; Dist=pdist(W); ％计算两两对象之间的距离 Tree=linkage(Dist,ward); ％建立层次化的结构树（类内平方距离最小误差） class=cluster(Tree,3); ％聚类 class_1=find(class==1); ％第一类 class_2=find(class==2); ％第二类 class_3=find(class==3); ％第三类 n1=size(class_1); n2=size(class_2); n3=size(class_3); ;By clustering: Background error probability (sample as the background, misjudged other) err_bg = 0.0420;