Abstract
This paper presents TSRuleGrowth, an algorithm for mining partially-ordered rules on a time series. TSRuleGrowth takes principles from the state of the art of transactional rule mining, and applies them to time series. It proposes a new definition of the support, which overcomes the limitations of previous definitions. Experiments on two databases of real data coming from connected environments show that this algorithm extracts relevant usual situations and outperforms the state of the art.
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CPU: Intel(R) Xeon(R) Gold 5118 @ 2.30 GHz, RAM: 128 GiB, Ubuntu 18.04.2 LTS.
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Vuillemin, B., Delphin-Poulat, L., Nicol, R., Matignon, L., Hassas, S. (2019). TSRuleGrowth: Mining Partially-Ordered Prediction Rules From a Time Series of Discrete Elements, Application to a Context of Ambient Intelligence. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2019. Lecture Notes in Computer Science(), vol 11888. Springer, Cham. https://doi.org/10.1007/978-3-030-35231-8_9
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