Genetic Approach for Fuzzy Mining Using Modified K-Means Clustering
نویسندگان
چکیده
A fuzzy-genetic data-mining algorithm for extracting both association rules and membership functions from quantitative transactions is shown in this paper. It used a combination of large 1-itemsets and membershipfunction suitability to evaluate the fitness values of chromosomes. The calculation for large 1itemsets could take a lot of time, especially when the database to be scanned could not totally fed into main memory. In this system, an enhanced approach, called the Modified cluster-based fuzzy-genetic mining algorithm. It divides the chromosomes in a population into clusters by the modified k-means clustering approach and evaluates each individual according to both cluster and their own information. Keywords— Modified k-means Clustering, data mining, fuzzy set, genetic algorithm, Fuzzy Association Rules, Quantitative transactions
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