Data Mining Techniques For Concisely Representing Patterns Sets

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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.

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