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Segmenting multi-attribute sequences using dynamic Bayesian networks
Robert Gwadera, Janne Toivola and Jaakko Hollmen
In: Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007), 28 Oct - 31 Oct 2007, Omaha, Nebraska, USA.
AbstractDiscovering dependencies between attributes in multiattribute
event sequences (multi-sequences), also known as
synchronized multi-stream sequences, is an important problem
in many domains, including monitoring systems and
molecular biology. Many real-life multi-sequences have a
segmental structure, with segments of differing complexities
of attribute dependencies, which reflects a changing nature
of the dependencies over time and space. In this paper we
propose a new approach for discovering dependencies in
multi-sequences which considers a possible segmental nature
of such dependencies and tries to describe the multisequences
in probabilistic terms using Dynamic Bayesian
Networks (DBN). To accurately quantify such changing dependencies, we segment the multi-sequence by fitting an optimal DBN for each segment. We use the Bayesian Information
Criterion (BIC) to select an optimal DBN structure and
the number of segments of the multi-sequence. [Edit]
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