  Association rules are useful to discover relationships, which are mostly
hidden, between the different items in large datasets. Symbolic models are the
principal tools to extract association rules. This basic technique is
time-consuming, and it generates a big number of associated rules. To overcome
this drawback, we suggest a new method, called MARC, to extract the more
important association rules of two important levels: Type I, and Type II. This
approach relies on a multi-topographic unsupervised neural network model as
well as clustering quality measures that evaluate the success of a given
numerical classification model to behave as a natural symbolic model.
