Taxonomy-driven lumping for sequence mining |
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Authors: | Francesco Bonchi Carlos Castillo Debora Donato Aristides Gionis |
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Affiliation: | (1) Yahoo! Research, Diagonal 177, Barcelona, 080018, Spain |
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Abstract: | Given a taxonomy of events and a dataset of sequences of these events, we study the problem of finding efficient and effective
ways to produce a compact representation of the sequences. We model sequences with Markov models whose states correspond to
nodes in the provided taxonomy, and each state represents the events in the subtree under the corresponding node. By lumping
observed events to states that correspond to internal nodes in the taxonomy, we allow more compact models that are easier
to understand and visualize, at the expense of a decrease in the data likelihood. We formally define and characterize our
problem, and we propose a scalable search method for finding a good trade-off between two conflicting goals: maximizing the
data likelihood, and minimizing the model complexity. We implement these ideas in Taxomo, a taxonomy-driven modeler, which we apply in two different domains, query-log mining and mining of moving-object trajectories.
The empirical evaluation confirms the feasibility and usefulness of our approach. |
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Keywords: | Data mining Sequence analysis Markov models Query-log analysis Spatial-data analysis |
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