M.: Exploratory data analysis leading towards the most interesting binary association rules

Abstract. Association Rules (AR) represent one of the most powerful and largely used approach to detect the presence of regularities and paths in large databases. Rules express the relations (in terms of co-occurence) between pairs of items and are defined in two parts: support and confidence. Most techniques for finding AR scan the whole data set, evaluate all possible rules and retain only rules that have support and confidence greater than thresholds, which should be fixed in order to avoid both that only trivial rules are retained and also that interesting rules are not discarded. This paper proposes a two steps interactive, graphical approach that uses factorial planes in the identification of potentially interesting items