A Possibilistic Approach for Mining Uncertain Temporal Relations from Diagnostic Evolution Databases

In this paper we propose a method for building possibilistic temporal constraint networks that better summarizes the huge set of mined timed-stamped sequences from a temporal data mining process. It belongs to the well-known second-order data mining problem, where the vast amount of simple sequences or patterns needs to be summarized further. It is a very important topic because the huge number of temporal associations extracted in the temporal data mining step makes the knowledge discovery process practically unmanageable for human experts. The method is based on the Theory of Evidence of Shafer as a mathematical tool for obtaining the fuzzy measures involved in the temporal network. This work also presents briefly a practical example describing an application of this proposal in the Intensive Care domain.