Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
ACM Transactions on Information Systems (TOIS)
A Survey of Temporal Knowledge Discovery Paradigms and Methods
IEEE Transactions on Knowledge and Data Engineering
Probabilistic Reasoning about Uncertain Relations between Temporal Points
TIME '01 Proceedings of the Eighth International Symposium on Temporal Representation and Reasoning (TIME'01)
A Possibility Theory-based Approach for Handling of Uncertain Relations Between Temporal Points
TIME '04 Proceedings of the 11th International Symposium on Temporal Representation and Reasoning
Extracting Uncertain Temporal Relations from Mined Frequent Sequences
TIME '06 Proceedings of the Thirteenth International Symposium on Temporal Representation and Reasoning
Temporal representation and reasoning in artificial intelligence: A review
Mathematical and Computer Modelling: An International Journal
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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.