Abstract
Traditional static pattern mining techniques, such as association rule mining and sequential pattern mining, perform inefficiently when applied to streaming data when regular updates are required, since there is significant repetition in the computation. Incremental mining techniques instead reuse information that has been previously extracted, and apply newly received data to compute the updated set of patterns. This paper proposes a new algorithm for incrementally mining sequential rules with streaming data. An existing rule mining algorithm, ERMiner is presented, and an incremental extension, called IERMiner is proposed and demonstrated. Experiments show that IERMiner significantly decreases the run time required to update the set of patterns when compared to running ERMiner on the full dataset each time.
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Drozdyuk, A., Buffett, S., Fleming, M.W. (2020). Incremental Sequential Rule Mining with Streaming Input Traces. In: Goutte, C., Zhu, X. (eds) Advances in Artificial Intelligence. Canadian AI 2020. Lecture Notes in Computer Science(), vol 12109. Springer, Cham. https://doi.org/10.1007/978-3-030-47358-7_8
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DOI: https://doi.org/10.1007/978-3-030-47358-7_8
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