Pattern Similarity in Time Interval Sequences

Luca Bonomi, Xiaoqian Jiang

Abstract: The extraction of temporal patterns from biomedical data is central in facilitating clinical decision support and enabling proactive interventions. Despite several approaches have been proposed for efficiently mining temporal patterns, most of the current solutions offer limited flexibility in addressing the noise in the data. To this end, we propose a similarity measure for temporal patterns in time interval sequences, we develop two mining approaches and conduct a preliminary evaluation on a real world dataset.