Dynamic order Markov model for categorical sequence clustering
Abstract Markov models are extensively used for categorical sequence clustering and classification due to their inherent ability to capture complex chronological jilungin dreaming tea dependencies hidden in sequential data.Existing Markov models are based on an implicit assumption that the probability of the next state depends on the preceding cont