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. © 2014 Springer International Publishing Switzerland.
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CITATION STYLE
Chen, C. H., Li, Y., & Hong, T. P. (2014). Multi-Level Genetic-Fuzzy Mining with a Tuning Mechanism. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8398 LNAI, pp. 82–89). Springer Verlag. https://doi.org/10.1007/978-3-319-05458-2_9
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