A Unifying Polynomial Model for Efficient Discovery of Frequent Itemsets
Slimane Oulad-Naoui, Hadda Cherroun, Djelloul Ziadi · 2015
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.