Clustering Sequences with Hidden Markov Models
Abstract
This paper discusses a probabilistic model-based approach to clus(cid:173) tering sequences, using hidden Markov models (HMMs) . The prob(cid:173) lem can be framed as a generalization of the standard mixture model approach to clustering in feature space. Two primary issues are addressed. First, a novel parameter initialization procedure is proposed, and second, the more difficult problem of determining the number of clusters K, from the data, is investigated. Experi(cid:173) mental results indicate that the proposed techniques are useful for revealing hidden cluster structure in data sets of sequences.
Cite
Text
Smyth. "Clustering Sequences with Hidden Markov Models." Neural Information Processing Systems, 1996.Markdown
[Smyth. "Clustering Sequences with Hidden Markov Models." Neural Information Processing Systems, 1996.](https://mlanthology.org/neurips/1996/smyth1996neurips-clustering/)BibTeX
@inproceedings{smyth1996neurips-clustering,
title = {{Clustering Sequences with Hidden Markov Models}},
author = {Smyth, Padhraic},
booktitle = {Neural Information Processing Systems},
year = {1996},
pages = {648-654},
url = {https://mlanthology.org/neurips/1996/smyth1996neurips-clustering/}
}