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
Keyword(s):
Data Mining, Frequent Itemsets, Formal Series, Weighted Automata, Algorithms, Unification
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Data Engineering
;
Data Management and Quality
;
Data Mining
;
Data Modeling and Visualization
;
Data Structures and Data Management Algorithms
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Information Systems
;
Modeling and Managing Large Data Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
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 c
an 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)