A generative probabilistic approach to visualizing sets of symbolic sequences

Authors:
Peter Tiño;Ata Kabán;Yi Sun
Affiliations:
The University of Birmingham, Birmingham, UK;The University of Birmingham, Birmingham, UK;University of Hertfordshire, Hatfield, UK
Venue:
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2004

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Abstract

There is a notable interest in extending probabilistic generative modeling principles to accommodate for more complex structured data types. In this paper we develop a generative probabilistic model for visualizing sets of discrete symbolic sequences. The model, a constrained mixture of discrete hidden Markov models, is a generalization of density-based visualization methods previously developed for static data sets. We illustrate our approach on sequences representing web-log data and chorals by J.S. Bach.