A Fuzzy Embedded GA for Information Retrieving from Related Data Set
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.
KeywordsGenetic Algorithm Fuzzy Rule Soft Computing Fuzzy Membership Fuzzy Membership Function
Unable to display preview. Download preview PDF.
- 3.Etzioni, S., Hanks, T., Jiang, R.M., Karp, O., Waarts, O.: Efficient Information Gathering on The Internet. In: 37th Annual Symposium on Foundations of Computer Science (FOCS 1996) (1996)Google Scholar
- 5.Pasi, G., Villa, R.: Personalized News Content Programming (PENG): A System Architecture. In: Andersen, K.V., Debenham, J., Wagner, R. (eds.) DEXA 2005. LNCS, vol. 3588, pp. 1008–1012. Springer, Heidelberg (2005)Google Scholar
- 6.Yager, R.: Misrepresentations and Challenges. IEEE Expert: Intelligent Systems and Their Application, pp. 41–42 (August 1994)Google Scholar