FCA and pattern structures for mining care trajectories

Aleksey Buzmakov (1) , Elias Egho (1) , Nicolas Jay (1) , Sergei O. Kuznetsov (2) , Amedeo Napoli (1) , Chedy Raïssi (1)

1 ORPAILLEUR - Knowledge representation, reasonning
2 Data Analysis and Artificial Intelligence

In this paper, we are interested in the analysis of sequential data and we propose an original framework based on Formal Concept Analysis (FCA). For that, we introduce sequential pattern structures, an original specification of pattern structures for dealing with sequential data. Pattern structures are used in FCA for dealing with complex data such as intervals or graphs. Here they are adapted to sequences. For that, we introduce a subsumption operation for sequence comparison, based on subsequence matching. Then, a projection, i.e. a kind of data reduction of sequential pattern structures, is suggested in order to increase the eficiency of the approach. Finally, we discuss an application to a dataset including patient trajectories (the motivation of this work), which is a sequential dataset and can be processed with the introduced framework. This research work provides a new and eficient extension of FCA to deal with complex (not binary) data, which can be an alternative to the analysis of sequential datasets

Mots clés: formal concept analysis; pattern structures; sequential pattern structures; sequences

Domaines: Intelligence artificielle [cs.AI]

Aleksey Buzmakov, Elias Egho, Nicolas Jay, Sergei O. Kuznetsov, Amedeo Napoli, et al.. FCA and pattern structures for mining care trajectories. Workshop FCA4AI, "What FCA can do for artificial intelligence?", Aug 2013, Beijing, China. ⟨hal-00910290⟩