DBHC: Discrete Bayesian HMM Clustering

Gabriel Budel, Flavius Frasincar*, David Boekestijn

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

Sequence data mining has become an increasingly popular research topic as the availability of data has grown rapidly over the past decades. Sequence clustering is a type of method within this field that is in high demand in the industry, but the sequence clustering problem is non-trivial and, as opposed to static cluster analysis, interpreting clusters of sequences is often difficult. Using Hidden Markov Models (HMMs), we propose the Discrete Bayesian HMM Clustering (DBHC) algorithm, an approach to clustering discrete sequences by extending a proven method for continuous sequences. The proposed algorithm is completely self-contained as it incorporates both the search for the number of clusters and the search for the number of hidden states in each cluster model in the parameter inference. We provide a working example and a simulation study to explain and showcase the capabilities of the DBHC algorithm. A case study illustrates how the hidden states in a mixture of HMMs can aid the interpretation task of a sequence cluster analysis. We conclude that the algorithm works well as it provides well-interpretable clusters for the considered application.

Original languageEnglish
Pages (from-to)3439-3454
Number of pages16
JournalInternational Journal of Machine Learning and Cybernetics
Volume15
Issue number8
DOIs
Publication statusPublished - 2024

Keywords

  • Graphical models
  • Mixture hidden Markov models
  • Probability smoothing
  • Sequence clustering
  • Sequence data mining

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