Many business organizations generate a huge amount of transaction data. Association rule mining is a powerful analysis tool to extract the useful meanings and associations from large databases and many automated systems have been developed for mining association rules. However, most of these systems usually mine many association rules from large databases and it is not easy for a user to extract meaningful rules. Visualization has become an important tool in the data mining process for extracting meaningful knowledge and information from large data sets. Though there are several techniques for visualizing mined association rules, most of these techniques visualize the entire set of discovered association rules on a single screen. Such a dense display can overwhelm analysts and reduce their capability of interpretation. In this paper we present a novel technique called VisAR for visualizing mined association rules. VisAR consists of four major stages for visualizing mined association rules. These stages include managing association rules, filtering association rules of interest, visualizing selected association rules, and interacting with the visualization process. Our technique allows an analyst to view only a particular subset of association rules which contain selected items of interest. VisAR is able to display not only many-to-one but also many-to-many association rules. Moreover, our technique can overcome problems of screen clutter and occlusion.
|Journal||Lecture notes in Artificial Intelligence (LNAI)|
|Publication status||Published - 2005|