Visual data analysis is an appealing and increasing field of application. We present two related visual analysis approaches that
allow for the visualization of graphical model parameters and time-dependent association rules. When the graphical model is defined over
purely nominal attributes, its local structure can be interpreted as an association rule. Such association rules comprise one of the most
prominent and wide-spread analysis techniques for pattern detection, however, there are only few visualization methods. We introduce an
alternative visual representation that also incorporates time since patterns are likely to change over time when the underlying data was
collected from real-world processes. We apply the technique to both an artificial and a complex real-life dataset and show that the combined
automatic and visual approach gives more and faster insight into the data than a fully-automatic approach only. Thus, our proposed method
is capable of reducing considerably the analysis time.