Summary
This chapter proposes a fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions. The number of membership functions for each item is not predefined, but can be dynamically adjusted. A GA-based framework for finding membership functions suitable for mining problems is proposed. The encoding of each individual is divided into two parts. The control genes are encoded into bit strings and used to determine whether membership functions are active or not. The parametric genes are encoded into real-number strings to represent membership functions of linguistic terms. The fitness of each set of membership functions is evaluated using the fuzzy-supports of the linguistic terms in the large 1-itemsets and the suitability of the derived membership functions. The suitability of membership functions considers overlap, coverage and usage factors.
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© 2008 Springer-Verlag Berlin Heidelberg
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Hong, TP., Chen, CH., Wu, YL., Tseng, V.S. (2008). Fining Active Membership Functions in Fuzzy Data Mining. In: Lin, T.Y., Xie, Y., Wasilewska, A., Liau, CJ. (eds) Data Mining: Foundations and Practice. Studies in Computational Intelligence, vol 118. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78488-3_11
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DOI: https://doi.org/10.1007/978-3-540-78488-3_11
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