Early detection of dementia allows people to have more time to prepare themselves for the symptom. As one of the methods to screen dementia, Category Fluency Test (CFT) is used to evaluate the organization of semantic memory and to assess the verbal fluency performance of patients with dementia. Recently, various measures to evaluate their CFT performance have been studied and, in particular, clusters and switches of the CFT data are considered as important factors. In this work, we analyze the clusters and switches of the CFT data by using Hidden Markov Model (HMM) to verify the hypothesis that a comprehensive pattern analysis of their switches and clusters can reveal important characteristics of verbal fluency performance.