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University of Laghouat, Algeria

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1 Papers

A Unifying Polynomial Model for Efﬁcient Discovery of Frequent Itemsets

Topics: Datamining; Modeling and Managing Large Data Systems; Data Modeling and Visualization; Data Structures and Data Management Algorithms

Authors: Slimane Oulad-Naoui ¹ , Hadda Cherroun ² and Djelloul Ziadi ³

Affiliations: ¹ Université de Ghardaia, Algeria , ² Université Amar Telidji, Algeria , ³ Normandie Université, France

Digital Object Identifier: 10.5220/0005516200490059

Proceedings of DATA 2015, ISBN: 978-989-758-103-8

Quick 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.

Related Ontology Subjects/Areas/Topics (16)

Artificial Intelligence	Data Modeling and Visualization	Health Information Systems
Biomedical Engineering	Data Structures and Data Management Algorithms	Modeling and Managing Large Data Systems
Business Analytics	Databases and Information Systems Integration	Sensor Networks
Data Engineering	Datamining	Signal Processing
Data Management and Quality	Enterprise Information Systems	Soft Computing
Data Mining