2.6. Hierarchical clustering of latent states

Based on the similarity measure, we built a hierarchical clustering tree of latent states. Hierarchical clustering groups data over a variety of scales by creating a cluster tree or ‘dendrogram’, a multilevel hierarchy in which clusters at one level are joined as clusters at the next level. The clustering procedure consisted of three steps: (i) define the similarity or dissimilarity between every pair of data points or strings in the data set; (ii) group the data or strings into a binary, hierarchical cluster tree; (iii) determine where to cut the hierarchical tree into clusters. Using the KL divergence metric, we applied a hierarchical clustering algorithm (MATLAB function: clusterdata.m) to define the similarity between latent states.