Abstract
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.
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Combi, C., Sabaini, A. (2013). Extraction, Analysis, and Visualization of Temporal Association Rules from Interval-Based Clinical Data. In: Peek, N., MarÃn Morales, R., Peleg, M. (eds) Artificial Intelligence in Medicine. AIME 2013. Lecture Notes in Computer Science(), vol 7885. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38326-7_35
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DOI: https://doi.org/10.1007/978-3-642-38326-7_35
Publisher Name: Springer, Berlin, Heidelberg
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