Jalilvand, Asal2021-04-292021-04-292021-04-29http://hdl.handle.net/10222/80445Sequential Rule Mining (SRM) discovers association relationship between items in a sequence database, w.r.t. their temporal order. Often, the high number of mined rules makes their exploration challenging. Visualization of Association Rules (ARs), a closely related field in data mining, has been studied extensively to address scalability issues; however, unlike Sequential Rules (SRs), the items in ARs are not partially ordered. The small body of research investigating SR visualization enforces many constraints on the rules that make their work less generalized. We tried to address this problem by combining matrix-based visualization of ARs and the partial order between rules items through topological sort. We developed an interactive system for mining and visualizing SRs. We experimented the effectiveness of our approach by conducting a user test and showing the reduced cognitive load for exploring SRs compared to the plain-text output of a popular off-the-shelf rule miner for a real-world dataset.enVisual AnalyticsData MiningSequential Rules