Extraction, Analysis, and Visualization of Temporal Association Rules from Interval-Based Clinical Data

Temporal association rules have been recently applied to interval-based temporal clinical data, to discover complex temporal relationships. In this paper, we first propose a refinement of the Data-Mining algorithm proposed by Sacchi et al. (2007) for the extraction of temporal association rules, improving the algorithm complexity in case of anti-monotonous rule support. Then, we address the non-trivial problem of displaying and visually analyzing this kind of data, through the use of an OLAP-based multidimensional model, and by proposing a visualization solution explicitly dealing with temporal association rules.