Summary
In this chapter, we gained knowledge about performing accurate cluster analysis in the MATLAB environment. Our exploration began by understanding the measurement of similarity, including concepts such as element proximity, similarity, and dissimilarity measures. We delved into different methods for grouping objects, namely hierarchical clustering, and partitioning clustering.
Regarding partitioning clustering, we focused on the k-means method. We learned how to iteratively locate k centroids, each representing a cluster. We also examined the effectiveness of cluster separation and how to generate a silhouette plot using cluster indices obtained from k-means. The silhouette value for each data point serves as a measure of its similarity to other points within its own cluster, compared to points in other clusters. Furthermore, we delved into k-medoids clustering, which involves identifying the centers of clusters using medoids instead of centroids. We learned the procedure...
