Abstract
A new approach to the problem of temporal knowledge induction from a collection of temporal events is presented. As a result, a set of frequent temporal patterns is obtained, represented following the Simple Temporal Problem (STP) formalism: a set of event types and a set of constraints describing common temporal arrangements between the events. The use of a clustering technique makes it possible to discriminate between the frequent patterns that are found in the collection.
This work was funded by the Spanish MICINN (TIN2009-14372-C03-03) and by the Xunta de Galicia (08SIN002206PR). M. R. Álvarez is funded by an FPU grant from the Spanish MEC (AP2008-02593).
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Álvarez, M.R., Félix, P., Cariñena, P., Otero, A. (2010). A Data Mining Algorithm for Inducing Temporal Constraint Networks. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Computational Intelligence for Knowledge-Based Systems Design. IPMU 2010. Lecture Notes in Computer Science(), vol 6178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14049-5_31
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DOI: https://doi.org/10.1007/978-3-642-14049-5_31
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