An interactive approach to display large sets of association rules

Knowledge Discovery in Databases (KDD) is an active research domain. Due to the number of large databases, various data raining methods were developed. Those tools can generate a large amount of knowledge that needs more advanced tools to be explored. We focus on association rules mining such as "If Antecedent then Conclusion" and more particularly on rules visualization during the post processing stage in order to help expert's analysis. An association rule is mainly calculated depending on two user-specified metrics: support and confidence. All current representations present a common limitation which is effective on small data quantities. We introduced a new interactive approach which combines both a global representation (2D matrix) and a detailed representation (Fisheyes view) in order to display large sets of association rules.