Abstract
In this paper, a two-stage multi-level genetic-fuzzy mining approach is proposed. In the first stage, the multi-level genetic-fuzzy mining (MLGFM) is utilized to derive membership functions of generalized items from the given taxonomy and transactions. In the second stage, the 2-tuples linguistic representation model is used to tune the derive membership functions. Experimental results on a simulated dataset show the effectiveness of the proposed approach.
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Chen, CH., Li, Y., Hong, TP. (2014). Multi-Level Genetic-Fuzzy Mining with a Tuning Mechanism. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds) Intelligent Information and Database Systems. ACIIDS 2014. Lecture Notes in Computer Science(), vol 8398. Springer, Cham. https://doi.org/10.1007/978-3-319-05458-2_9
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DOI: https://doi.org/10.1007/978-3-319-05458-2_9
Publisher Name: Springer, Cham
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