A Novel Hidden Markov Model-Based Hierarchical Time-Series Clustering Algorithm

In this paper, a novel hidden Markov model (HMM)-based hierarchical time-series clustering algorithm HBHCTS is proposed, because of the disadvantage of traditional HMM-based clustering algorithms for time-series. The main purpose is to improve clustering quality and represent the clusters easily at the same time. In HBHCTS, HMMs are built from time-series, and the initial models are obtained according to the most similarity, and then the process of merging and updating initial models is iterated until the final result is obtained. In the experiment, the relation between correctness rate and the length of a sequence, the relation between correctness rate and the model distance are researched. The results show that the HBHCTS can achieve better performance in correctness rate than the traditional HMM-based clustering algorithm.