combining visual techniques for association rules exploration
The abundance of data available nowadays fosters the need of developing tools and methodologies to help users in extracting significant information. Visual data mining is going in this direction, exploiting data mining algorithms and methodologies together with information visualization techniques.The demand for visual and interactive analysis tools is particularly pressing in the Association Rules context where often the user has to analyze hundreds of rules in order to grasp valuable knowledge. This paper presents a visual strategy to face this drawback by exploiting graph-based technique and parallel coordinates to visualize the results of association rules mining algorithms. The combination of the two approaches allows both to get an overview on the association structure hidden in the data and to deeper investigate inside a specific set of rules selected by the user.
combinational visualization association association rules rules exploration data tools methodology help user user informal visual data mining data mining direct algorithm information visualization interaction analysis context order knowledge paper strategies parallel coordinates parallel coordination association rule mining structure graph drawing users graph