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Nowadays data sets are available in very complex and heterogeneous ways.Mining of such data collections is essential to support many real-worldapplications ranging from healthcare to marketing. In this work, we focus onthe analysis of "complex" sequential data by means of interesting sequentialpatterns. We approach the problem using the elegant mathematical framework ofFormal Concept Analysis (FCA) and its extension based on "pattern structures".Pattern structures are used for mining complex data (such as sequences orgraphs) and are based on a subsumption operation, which in our case is definedwith respect to the partial order on sequences. We show how pattern structuresalong with projections (i.e., a data reduction of sequential structures), areable to enumerate more meaningful patterns and increase the computingefficiency of the approach. Finally, we show the applicability of the presentedmethod for discovering and analyzing interesting patient patterns from a Frenchhealthcare data set on cancer. The quantitative and qualitative results (withannotations and analysis from a physician) are reported in this use case whichis the main motivation for this work.  Keywords: data mining; formal concept analysis; pattern structures;projections; sequences; sequential data.

On mining complex sequential data by means of FCA and pattern structures