We study uncertainty models in sequential pattern mining. We consider situations where there is uncertainty either about a source or an event. We show that both these types of uncertainties could be modelled using probabilistic databases, and give possible-worlds semantics for both. We then describe "interestingness" criteria based on two notions of frequentness (previously studied for frequent itemset mining) namely expected support [C. Aggarwal et al. KDD'09;Chui et al., PAKDD'07,'08] and probabilistic frequentness [Bernecker et al., KDD'09]. We study the interestingness criteria from a complexity-theoretic perspective, and show that in case of source-level uncertainty, evaluating probabilistic frequentness is #P-complete, and thus no polynomial time algorithms are likely to exist, but evaluate the interestingness predicate in polynomial time in the remaining cases. © 2010 Springer-Verlag.
CITATION STYLE
Muzammal, M., & Raman, R. (2010). On probabilistic models for uncertain sequential pattern mining. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6440 LNAI, pp. 60–72). https://doi.org/10.1007/978-3-642-17316-5_6
Mendeley helps you to discover research relevant for your work.