Abstract
In this paper, we present a fuzzy data mining approach for extracting association rules from quantitative data using search tree technique. Fuzzy association rule is used to solve the high dimensional problem by allowing partial memberships to each different set. It suffers from exponential growth of search space, when the number of patterns and/or variables becomes high. This increased search space results in high space complexity. To overcome this problem, the proposed method uses search tree technique to list all possible frequent patterns from which the fuzzy association rules have been generated.
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Rajeswari, A.M., Karthika Devi, M.S., Deisy, C. (2012). Mining of Quantitative Association Rule on Ozone Database Using Fuzzy Logic. In: Balasubramaniam, P., Uthayakumar, R. (eds) Mathematical Modelling and Scientific Computation. ICMMSC 2012. Communications in Computer and Information Science, vol 283. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28926-2_55
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DOI: https://doi.org/10.1007/978-3-642-28926-2_55
Publisher Name: Springer, Berlin, Heidelberg
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