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PREDICTION METHOD WITH THE VARIABLE THRESHOLD BASED ON FUZZY ASSOCIATION RULES

机译:基于模糊关联规则的变量阈值的预测方法

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Quantitative attributes are partitioned into several fuzzy sets by fuzzy c-means algorithm, and the search technology of Apriori algorithm is improved to discover interesting fuzzy association rules. Then, the prediction method with the variable threshold based on the fuzzy association rules is presented. In this prediction method, a little error between prediction value and actual value is allowed. When the error is less than a given threshold, prediction value is regarded as acceptable or rational. The parameters of triangular fuzzy numbers are adjusted to improve the prediction accuracy by the genetic algorithm last. This prediction method can obtain the different prediction accuracy corresponding to the different error threshold chosen by the users, so it is more flexible and effective.
机译:通过模糊C型算法将定量属性分成多个模糊集,并且提高了APRiori算法的搜索技术,以发现有趣的模糊关联规则。然后,呈现了基于模糊关联规则的具有可变阈值的预测方法。在这种预测方法中,允许预测值和实际值之间的误差。当误差小于给定阈值时,预测值被视为可接受或理性的。调整三角模糊数的参数以通过遗传算法提高预测精度。该预测方法可以获得与用户选择的不同误差阈值相对应的不同预测精度,因此它更灵活且有效。

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