Data Mining Techniques For Concisely Representing Patterns Sets
A Detailed Exploration Of The Conjunctive And The Disjunctive Search Spaces
This book focuses on mining frequent itemsets and association rules.
A detailed study we carry out shows that closed itemsets and minimal generators play a key role in concisely representing patterns sets.
However, an intra-class combinatorial redundancy would logically results from the inherent absence of a unique minimal generator associated to a given closed itemset.
In this respect, we propose lossless reductions of the minimal generator set thanks to a new substitution- based process.
Our theoretical results will then be extended to the association rule framework.
We also lead a thorough exploration of the disjunctive search space, where itemsets are characterized by their respective disjunctive supports, instead of the conjunctive ones.
In order to obtain a redundancy-free representation of the disjunctive search space, an interesting solution consists in selecting a unique element to represent itemsets covering the same set of data.
We then introduce a new operator dedicated to this task.
This operator is at the roots of new concise representations of frequent itemsets and is used for the derivation of generalized association rules.
Tarek Hamrouni obtained his Ph.D in Computer Science from the El-Manar University (Tunis, Tunisia) and the University of Artois (Lens, France) in 2009.
Currently, he is an Assistant Professor in Computer Science at La Manouba''s University (Tunisia).
He is interested in data mining and artificial intelligence.