Abstract
In an industrial context application aiming at performing aeronautic maintenance tasks scheduling, we propose a frequent Interval Time Sequences (ITS) extraction technique from discrete temporal sequences using a sliding window approach to relax time constraints. The extracted sequences offer an interesting overview of the original data by allowing a temporal leeway on the extraction process. We formalize the ITS extraction under classical time and support constraints and conduct some experiments on synthetic data to validate our proposal.
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Ben Zakour, A., Maabout, S., Mosbah, M., Sistiaga, M. (2014). Relaxing Time Granularity for Mining Frequent Sequences. In: Guillet, F., Pinaud, B., Venturini, G., Zighed, D. (eds) Advances in Knowledge Discovery and Management. Studies in Computational Intelligence, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-319-02999-3_4
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DOI: https://doi.org/10.1007/978-3-319-02999-3_4
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