Research And Application Of Data Mining Algorithm In Retail Industry

Nowadays,with the rapid development of science and technology,electronic technology and technology have been throughout people's lives,resulting in a huge amount of electronic data and information.In these data,there are a lot of information and laws worth exploring,but blind search will only find needles in a haystack,and data mining can find value information from the data.This information is conducive to the decision-making and further analysis of customers.The main work of this paper is to apply data mining technology to the retail industry.There are many algorithms in data mining technology,which are also applied in retail industry.This paper mainly studies clustering analysis,association rule analysis and rough set theory.Including combining with RFM customer value analysis model,using cluster analysis to divide customer groups into different categories;using association rule analysis to explore potential information in commodity trading data;using rough set theory to simplify data sets in massive data.Firstly,this paper introduces the background of the research and the current situation of data mining technology at home and abroad and its application in the retail industry,and then introduces the data mining technology briefly.The clustering algorithm used in this paper is k-means algorithm.Through the analysis and research of the traditional algorithm,and in order to overcome the shortcomings of the algorithm,an improved method of selecting the initial parameters of the algorithm is proposed.The improvement of this method is mainly aimed at the selection of the clustering result value K and the initial clustering center point in the traditional K-means algorithm.By giving the selection basis,the clustering algorithm can be more stable and not affected by the fluctuation of random parameters.Based on the frequent pattern tree,an improved FP growth algorithm is proposed.According to the simulation experiment,the operation time of the improved algorithm and the traditional algorithm is compared,and the performance of the improved algorithm is evaluated.This paper analyzes the theory of rough set,and applies it to the data of commodity transaction,and analyzes the importance of decision factors for huge data sources,so as to simplify the data set.In this paper,through clustering algorithm and RFM customer value analysis model,the clustering analysis of customer group based on value index is carried out.Through the analysis of association rules,the mining model of association rules is established,and the frequent item set mining and association rules extraction of commodity transaction data are carried out,and the research results are obtained through the experimental data.The idea put forward in this paper has certain rationality and practical significance in application.