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A Unifying Polynomial Model for Efﬁcient Discovery of Frequent ItemsetsTopics: Data Modeling and Visualization; Data Structures and Data Management Algorithms; Datamining; Modeling and Managing Large Data Systems

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Subjects/Areas/Topics:Artificial Intelligence
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Biomedical Engineering
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Data Engineering
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Data Management and Quality
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Data Mining
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Data Modeling and Visualization
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Data Structures and Data Management Algorithms
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Databases and Information Systems Integration
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Datamining
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Modeling and Managing Large Data Systems
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Abstract: It is well-known that developing a unifying theory is one of the most important issues in Data Mining research.
In the last two decades, a great deal has been devoted to the algorithmic aspects of the Frequent Itemset (FI)
Mining problem. We are motivated by the need of formal modeling in the field. Thus, we introduce and
analyze, in this theoretical study, a new model for the FI mining task. Indeed, we encode the itemsets as words
over an ordered alphabet, and state this problem by a formal series over the counting semiring (N,+,x,0,1),
whose the range constitutes the itemsets and the coefficients their supports. This formalism offers many advantages
in both fundamental and practical aspects: The introduction of a clear and unified theoretical framework
through which we can express the main FI-approaches, the possibility of their generalization to mine other
more complex objects, and their incrementalization and/or parallelization; in practice, we explain how this
problem can be seen as that of word recognition by an automaton, allowing an efficient implementation in
O(|Q|) space and O(|FL||Q|]) time, where Q is the set of states of the automaton used for representing the
data, and FL the set of prefixial maximal FI.(More)

It is well-known that developing a unifying theory is one of the most important issues in Data Mining research. In the last two decades, a great deal has been devoted to the algorithmic aspects of the Frequent Itemset (FI) Mining problem. We are motivated by the need of formal modeling in the field. Thus, we introduce and analyze, in this theoretical study, a new model for the FI mining task. Indeed, we encode the itemsets as words over an ordered alphabet, and state this problem by a formal series over the counting semiring (N,+,x,0,1), whose the range constitutes the itemsets and the coefficients their supports. This formalism offers many advantages in both fundamental and practical aspects: The introduction of a clear and unified theoretical framework through which we can express the main FI-approaches, the possibility of their generalization to mine other more complex objects, and their incrementalization and/or parallelization; in practice, we explain how this problem can be seen as that of word recognition by an automaton, allowing an efficient implementation in O(|Q|) space and O(|FL||Q|]) time, where Q is the set of states of the automaton used for representing the data, and FL the set of prefixial maximal FI.

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Guests can use SciTePress Digital Library without having a SciTePress account. However, guests have limited access to downloading full text versions of papers and no access to special options.

Oulad-Naoui, S.; Cherroun, H. and Ziadi, D. (2015). A Unifying Polynomial Model for Efﬁcient Discovery of Frequent Itemsets. In Proceedings of 4th International Conference on Data Management Technologies and Applications - DATA, ISBN 978-989-758-103-8; ISSN 2184-285X, pages 49-59. DOI: 10.5220/0005516200490059

@conference{data15, author={Slimane Oulad{-}Naoui. and Hadda Cherroun. and Djelloul Ziadi.}, title={A Unifying Polynomial Model for Efﬁcient Discovery of Frequent Itemsets}, booktitle={Proceedings of 4th International Conference on Data Management Technologies and Applications - DATA,}, year={2015}, pages={49-59}, publisher={SciTePress}, organization={INSTICC}, doi={10.5220/0005516200490059}, isbn={978-989-758-103-8}, issn={2184-285X}, }

TY - CONF

JO - Proceedings of 4th International Conference on Data Management Technologies and Applications - DATA, TI - A Unifying Polynomial Model for Efﬁcient Discovery of Frequent Itemsets SN - 978-989-758-103-8 IS - 2184-285X AU - Oulad-Naoui, S. AU - Cherroun, H. AU - Ziadi, D. PY - 2015 SP - 49 EP - 59 DO - 10.5220/0005516200490059