Literature Review on Hidden Markov Model-Based Sequential Data Clustering

Abstract

The purpose of this report is to investigate current existing algorithm to cluster sequential data based on hidden Markov model (HMM). Clustering is a classic technique that divides a set of objects into groups (called clusters) so that objects in the same cluster are similar in some sense. The clustering of sequential or time series data, however, draws lately more and more attention from researchers. Hidden Markov model (HMM)-based clustering of sequences is probabilistic model-based approach to clustering sequences. Generally, there are two kinds of methodologies: parametric and semi-parametric. The parametric methods make strict assumptions that each cluster is represented by a corresponding HMM, while the semi-parametric approaches relax this assumption and transform the problem to a similarity-based issue. Generally, the semi-parametric methods perform better than parametric approaches as reported by some researchers. Future research can be done in exploring new distance measures between sequences and extending current HMM-based methodologies by using other models.