Design And Implementation Of Association Rule Mining Algorithm Based On Interest Degree

With the rapid development of Internet technology, mobile Internet era has arrived. Data mining technology has aroused great concern and repercussions of society. With the continuous development of data mining technology, it has been divided into different research directions, such as classification, clustering,association rules mining and so on.As an important part of data mining, the purpose of association rule mining is to find the direct and interesting relationship between items and items from a large amount of data.The traditional association rule mining algorithm is based on the support confidence framework to find the itemsets of higher frequency, typical representation is the Apriori algorithm, but this tends to ignore low support - high correlation of association rules. In view of the above problems, some scholars put forward the positive correlation pairs based on Phi correlation coefficient, it can dig correlation pairs with low support but high correlation. Although the algorithm has cut the search space, it is not obvious that the performance of the operation time based on the big data set is reduced.Then this paper presents a new association rules mining algorithm based on interest, using a upper bound on interest of the supersets to prune the search space.It greatly reduces the running time of the algorithm, and filters the meaningless correlation pairs according to the constraints of the redundancy. Compared with the algorithm based on the Phi correlation coefficent, the new algorithm has been significantly improved in reducing the running time, and the result has cut the redundant correlation pairs. So it improved the mining efficiency and accuracy. The association rule algorithm is applied to the field of meteorological data quality control. The association rules algorithm is used to mine the historical meteorological data, and all the correlation pairs are extracted to form a sample database, and these construct the model of the meteorological data quality control. By comparing with the traditional quality control method, it is found that the quality control model based on the new algorithm of association rules can greatly improve detection rate,sensitivity and even time performance,it can find abnormal data quickly and accurately.Finally the improved association rule algorithm based on the meteorological data quality control model is designed as a meteorological data quality control prototype system. Through the realization of the extraction of association rules, the detection of abnormal records, and evaluate the effect of quality control, it verify the feasibility of such quality control model.Through above research, the automatic management and quality control of meteorological observation data are realized in result from the combination of association rules algorithm and computer technology. At the same time to realize the standardization management of meteorological data, the system enhances the quality control effect of meteorological observation data, and improves the processing efficiency of meteorological data...