Based On The Research Of Data Mining Method Of Association Rules

In the era of big data,how human beings access their required information is essential,which results in the emerging of data mining technology.As an important part of data mining technology,association rule mining technology is mainly used in the recommendation system,such as the placement of supermarket goods.By analyzing a large number of data of the shopping basket,we can figure out what items are often purchased together,and then put these items in the same position,thus increasing the sales of these goods.This paper discusses the data mining related technologies,including classification,clustering,outlier analysis,etc.Then,the current classic algorithms,such as data mining,the basic flow of data mining and the evaluation of data mining algorithms,are being introduced.In the analysis of association rule mining algorithm,this paper first focuses on the Apriori algorithm,and realizes the algorithm by using the supermarket shopping basket data.Taking into account the drawbacks of the Apriori algorithm in the performance,this paper then introduces the FP-growth algorithm,and constructs the frequent pattern base,conditional FP-tree,and frequent patterns using the generated FP-tree.Finally,the corresponding association rules are revealed.The algorithm is implemented by using the development tool,Python,and the mining results of the two algorithms are compared experimentally.Considering the correlation between data item of the shopping baskets,this paper measure the correlation through increasing degree,puts forward the negative association rules mining algorithm based on this algorithm,and verifies the improved algorithm.Results indicates that the algorithm can effectively find a negative association rules.The algorithm takes the condition,some goods will not be bought under the premise of other specific goods having been bought,into consideration.Thus,the supermarket workers try to put them in a different location.In conclusion,this algorithm provides a new way of thinking to the supermarket goods put problems.