Extracting Uncertain Temporal Relations from Mined Frequent Sequences

In this work we address an approach for solving the problem of building a temporal constraint network from the set of frequent sequences obtained after a temporal data mining process. In particular, the temporal data mining algorithm used is TSET [7], an algorithm based on the inter-transactional framework that uses a unique tree-based structure to discover frequent sequences from datasets. The model of temporal network is the proposed by Hadjali, Dubois and Prade [8] where each constraint is formed by three possibility values expressing the relative plausibility of each basic relations between two point-based events, that is, "before", "at the same time" and "after". We propose the use of the Shafer Theory for computing the possibility values of the temporal relations involved in the network from the calculated probability masses of the sequences. The final goal is to obtain a more understandable and useful sort of knowledge from a huge volume of temporal associations resulting after the data mining process.