Cluster analysis | MATLAB realizes the visualization of the clustering results of the GMM Gaussian distribution mixture model

Cluster analysis | MATLAB realizes the visualization of the clustering results of the GMM Gaussian distribution mixture model

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basic introduction

Cluster analysis | MATLAB realizes the visualization of clustering results of GMM Gaussian distribution mixture model, GMM clustering, visualization of clustering results, MATLAB program.
GMM (Gaussian Mixture Model) is a probabilistic model-based clustering method that treats data as a mixture model composed of multiple Gaussian distributions, and optimizes parameters through maximum likelihood estimation. There is no need to specify the number of clusters in advance, but the number of clusters and their parameters in the data are automatically estimated. This flexibility makes GMM suitable for a variety of different types and shapes of data distributions.

programming

• Complete source code and data acquisition method: Reply to MATLAB via private message to visualize the clustering results of the GMM Gaussian distribution mixture model .

%%  清空环境变量
warning off             % 关闭报警信息
close all               % 关闭开启的图窗
clear                   % 清空变量
clc                     % 清空命令行

cosD = pdist(meas,'cosine');
clustTreeCos = linkage(cosD,'average');
cophenet(clustTreeCos,cosD)

ans =

    0.9360
[h,nodes] = dendrogram(clustTreeCos,0);
h_gca = gca;
h_gca.TickDir = 'out';
h_gca.TickLength = [.002 0];
h_gca.XTickLabel = [];
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版权声明：本文为CSDN博主「机器学习之心」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/kjm13182345320/article/details/119920826



figure
hidx = cluster(clustTreeCos,'criterion','distance','cutoff',.006);
for i = 1:5
    clust = find(hidx==i);
    plot3(meas(clust,1),meas(clust,2),meas(clust,3),ptsymb{
    
    i});
    hold on
end
hold off
xlabel('Sepal Length');
ylabel('Sepal Width');
zlabel('Petal Length');
view(-137,10);
grid on

————————————————
版权声明：本文为CSDN博主「机器学习之心」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/kjm13182345320/article/details/119920826

References

[1] https://blog.csdn.net/kjm13182345320/article/details/129215161
[2] https://blog.csdn.net/kjm13182345320/article/details/128105718