Providing efficient and easy-to-use graphical tools to users is a promising challenge of data mining, especially in the case of association rules. These tools must be able to generate explicit knowledge and, then, to present it in an elegant way. Visualization techniques have shown to be an efficient solution to achieve such a goal. Even though considered as a key step in the mining process, the visualization step of association rules received much less attention than that paid to the extraction step. Nevertheless, some graphical tools have been developed to extract and visualize association rules. In those tools, various approaches are proposed to filter the huge number of association rules before the visualization step. However both data mining steps (association rule extraction and visualization) are treated separately in a one way process. Recently different approaches have been proposed that use meta-knowledge to guide the user during the mining process. Standing at the crossroads of Data Mining and Human-Computer Interaction, those approaches present an integrated framework covering both steps of the data mining process. This chapter describes and discusses such approaches. Two approaches are described in details: the first one builds a roadmap of compact representation of association rules from which the user can explore generic bases of association rules and derive, if desired, redundant ones without information loss. The second approach clusters the set of association rules or its generic bases, and uses a fisheye view technique to help the user during the mining of association rules. Generic bases with their links or the associated clusters constitute the meta-knowledge used to guide the interactive and cooperative visualization of association rules.
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