Abstract
The arm of this work is to provide a formal model and an effective way for information retrieving from a big related data set. Based upon fuzzy logic operation, a fuzzy mathematical model of 0-1 mixture programming is addressed. Meanwhile, a density function indicating the overall possessive status of the effective mined out data is introduced. Then, a soft computing (SC) approach which is a genetic algorithm (GA) embedded fuzzy deduction is presented. During the SC process, fuzzy logic decision is taken into the uses of determining the genes’ length, calculating fitness function and choosing feasible solution. Stimulated experiments and comparison tests show that the methods can match the user’s most desired information from magnanimity data exactly and efficiently. The approaches can be extended in practical application in solving general web mining problem.
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Yi, Y., Mei, J., Xiao, Z. (2006). A Fuzzy Embedded GA for Information Retrieving from Related Data Set. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_90
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DOI: https://doi.org/10.1007/11925231_90
Publisher Name: Springer, Berlin, Heidelberg
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