MATLAB Code Implementation for Cluster Analysis
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Resource Overview
Hierarchical Clustering Method and Optimal Partitioning Application Development for Sample Clustering
Detailed Documentation
This content discusses cluster analysis and hierarchical clustering methods. We can develop an application to achieve optimal partitioning for sample clustering, which may involve implementing algorithms such as agglomerative (bottom-up) or divisive (top-down) hierarchical approaches using MATLAB functions like linkage() and cluster(). Additionally, we can explore other clustering algorithms and techniques—such as k-means (using kmeans()), DBSCAN, or Gaussian mixture models (fitgmdist())—to enhance the accuracy and reliability of results through methods like silhouette analysis or cross-validation.
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