Abstract
In fuzzy data mining, the membership function significantly influences exploration performance. Therefore, some scholars have proposed genetic-fuzzy mining to determine a set of good membership functions for effectively mining fuzzy association rules. Some scholars proposed evaluating the membership functions using both the number of large 1-itemsets and the suitability of chromosomes. They only considered large 1-itemsets instead of all large itemsets because of the time-consuming problem. We analyzed the time-consuming reason and found that there are many independent calculations in the mining process. Given this, we adopt the GPU devices and propose a GPU-based mining algorithm with evaluation on all large itemsets to improve obtained membership functions and reduce time cost. Experimental results also show the efficiency of using GPUs on genetic-fuzzy data mining.
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Chen, CH., Huang, YQ., Hong, TP. (2022). Using GPUs to Speed Up Genetic-Fuzzy Data Mining with Evaluation on All Large Itemsets. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13757. Springer, Cham. https://doi.org/10.1007/978-3-031-21743-2_2
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