In this chapter, we demonstrate how we can identify and study clusters within Transition Network Analysis (TNA) to reveal the underlying heterogeneity in learners’ behavioral patterns. Specifically, we rely on mixture Markov models (MMM) to identify latent subgroups characterized by unique transition probabilities, a method that can also incorporate covariates to explain the identified clusters. We employ the tna R package to understand the distinct transition dynamics between states or events in each cluster through the study of centrality measures, communities and cliques. Lastly, we exemplify how to implement other forms of clustering (e.g., distance based) and grouping, as well as other types of transition networks (e.g., frequency-based transition networks).