Using a Reinforced Concept Lattice to Incrementally Mine Association Rules from Closed Itemsets

In the Data Mining area, discovering association rules is one of the most important task. It is well known that the number of these rules rapidly grows to be unwieldy as the frequency requirements become less strict,especially when collected data is highly correlated or dense. Since a big number of the frequent itemsets, from which association rules can be generated, turn out to be redundant, it is sufficient to consider only the rules among closed frequent itemsets or concepts. In order to efficiently generate the association rules, it is often essential to know the Concept Lattice, that also allows the user to better understand the relationships between the closed itemsets. We propose an incremental algorithm that mines all the closed itemsets, reading the data only once. The Concept Lattice is incrementally updated using a simple but essential structure directly connected to it. This structure allows to speed up the execution time and makes the algorithm applicable on both static and dynamic (stream data) very dense datasets.