Abstract
Given market or log data, it is very useful to find two sets of items or events that occur frequently with a regular time interval. We call a time-dependent relationship between two itemsets a time interval-based association rule. Finding time interval-based association rules, however, has not been much investigated yet until now. In this paper, we propose an efficient method for finding time interval-based association rules. The proposed method transforms the original input data into a more efficient form and then utilizes the transformed data in the subsequent steps. As a result, the input/output (I/O) cost of reading the data from disk is significantly reduced. Our experiments demonstrate the efficiency of the proposed method compared with those of the existing methods.
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Acknowledgements
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2015R1C1A1A02037071).
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Lee, K.Y., Suh, YK. (2019). Efficient Mining of Time Interval-Based Association Rules. In: Lee, W., Leung, C. (eds) Big Data Applications and Services 2017. BIGDAS 2017. Advances in Intelligent Systems and Computing, vol 770. Springer, Singapore. https://doi.org/10.1007/978-981-13-0695-2_13
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DOI: https://doi.org/10.1007/978-981-13-0695-2_13
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