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On probabilistic models for uncertain sequential pattern mining

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conference contribution
posted on 28.10.2014, 10:26 by Muhammad Muzammal, Rajeev Raman
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.

History

Citation

Advanced Data Mining and Applications Lecture Notes in Computer Science Volume 6440, 2010, pp 60-72

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Computer Science

Source

6th International Conference, ADMA 2010, Chongqing, China, November 19-21, 2010

Version

AM (Accepted Manuscript)

Published in

Advanced Data Mining and Applications Lecture Notes in Computer Science Volume 6440

Publisher

Springer Verlag

issn

0302-9743

isbn

978-3-642-17315-8;978-3-642-17316-5

Copyright date

2010

Available date

28/10/2014

Publisher version

http://link.springer.com/chapter/10.1007/978-3-642-17316-5_6

Editors

Cao, L.;Feng, Y.;Zhong, J.

Book series

Lecture Notes in Computer Science;

Temporal coverage: start date

19/11/2010

Temporal coverage: end date

21/11/2010

Language

en