In this chapter, we learned how to perform an accurate cluster analysis in the MATLAB environment. First, we explored how to measure similarity. We learned concepts such as proximity between elements, similarity and dissimilarity measures, and Euclidean, Minkowski, Manhattan, and cosine distance metrics. We looked at a couple of methods for grouping objects: hierarchical clustering and partitioning clustering. In the first method, clusters are constructed by recursively partitioning the instances in either a top-down or bottom-up fashion. The second one decomposes a dataset into a set of disjoint clusters.
We discovered hierarchical clustering in MATLAB using the
cluster functions. These functions perform agglomerative clustering. We learned how to calculate the distance between the objects through the
pdist function. To determine the proximity of objects to each other, we used the linkage function. With the cluster function, we cut the ramifications from the bottom...