This paper presents a research work in the domains of se- quential pattern mining and formal concept analysis. Using a combined method, we show how concept lattices and interestingness measures such as stability can improve the task of discovering knowledge in symbolic sequential data. We give example of a real medical application to illus- trate how this approach can be useful to discover patterns of trajectories of care in a french medico-economical database.
['Elias Egho', 'Nicolas Jay', 'Chedy Raïssi', 'Amedeo Napoli']
A FCA-based analysis of sequential care trajectories