3D Matrix-Based Visualization System of Association Rules

With the growing number of mining datasets, it becomes increasingly difficult to explore interesting rules because of the large number of resultant and its nature complexity. Studies on human perception and intuition show that graphical representation could be a better illustration of how to seek information from the data using the capabilities of human visual system. In this work, we present and implement a 3D matrix-based approach visualization system of association rules. The main visual representation applies the extended matrix-based approach with rule-to-items mapping to general transaction data set. A novel method merging rules and assigning weight is proposed in order to reduce the dimension of the association rules, which will help users to find more important items in the new rule. Furthermore, several interactions such as sorting, filtering, zoom and rotation, facilitate decision-makers to explore the rules which are of interest in various aspects. Finally, extensive evaluations have been conducted to assess the system from a logical reasoning point of view.