Association Rules Optimization Algorithm Based on Fuzzy Clustering

Abstract

Frequent pattern mining has been an important research direction in association rules. This paper use a methodology by preprocessing the original dataset using fuzzy clustering which can mapped quantitative datasets into linguistic datasets. Then we propose a algorithm based on fuzzy frequent pattern tree for extracting fuzzy frequent itemset from mapped linguistic datasets. Experimental results show that our algorithm is shorter than the F-Apriori on computing time to huge database. For large database, the algorithm presented in this paper is proved to have a good prospect.

Research on the intelligent control method of wind turbine grid connection without load

“…Based on the fuzzy reasoning realized by the neural network structure, the generated model neural control has the function of nonlinear control and the ability of self-learning and self-adaptation of the neural network. The initial control rules are obtained through the nearest neighbor clustering algorithm, and then the parameters of the control rules are input into the neural network for parameter adjustment [8]. In the algorithm, the first data is first used as the clustering center of the first group.…”