Visual Analysis of User-Driven Association Rule Mining

Association rules have been widely used for detecting relations between attribute-value pairs of categorical datasets. Existing solutions of mining interesting association rules are based on the support-confidence theory. However, it is non-trivial for the user to understand and modify the rules or the results of intermediate steps in the mining process, because the interestingness of rules might differ largely for various tasks and users. In this paper we propose to reinforce conventional association rule mining process by mapping the entire process into a visualization assisted loop, with which the user workload for modulating parameters and mining rules is reduced, and the mining efficiency is greatly improved. A matrix-based visualization technique is employed to encode the measure computation value, the data distribution and the intermediate results. We also design a set of visual exploration tools to support interactively inspection and manipulation of association measures, constraints of different types, and the results of intermediate steps. The effectiveness of our approach is demonstrated with various scenarios.