  In the last few years, the amount of collected data, in various computer
science applications, has grown considerably. These large volumes of data need
to be analyzed in order to extract useful hidden knowledge. This work focuses
on association rule extraction. This technique is one of the most popular in
data mining. Nevertheless, the number of extracted association rules is often
very high, and many of them are redundant. In this paper, we propose a new
algorithm, called PRINCE. Its main feature is the construction of a partially
ordered structure for extracting subsets of association rules, called generic
bases. Without loss of information these subsets form representation of the
whole association rule set. To reduce the cost of such a construction, the
partially ordered structure is built thanks to the minimal generators
associated to frequent closed patterns. The closed ones are simultaneously
derived with generic bases thanks to a simple bottom-up traversal of the
obtained structure. The experimentations we carried out in benchmark and "worst
case" contexts showed the efficiency of the proposed algorithm, compared to
algorithms like CLOSE, A-CLOSE and TITANIC.
