The Research Of Shopping Data Mining Based On Passive RFID Tags

In recent years,non-contact radio frequency identification technology(Radio Frequency Identification,RFID)has become an indispensable part of people's lives,by virtue of their small s ize,long-distance communication,wireless identification,with a certain storage capacity and without manual maintenance and many other features,RFID has become an important part of the field of information data collection.With the rapid development of Internet of things(Io T)in China's,RFID has been widely used in managing supply chains,tracking livestock,preventing fake,access control systems,automatic checkout and tracking library books and many other areas.Passive RFID will take an important place at the mall with no built-in battery,wireless identification and other advantages.This artic le applies the RFID system to the shopping data deep analysis,through the real-time analysis of shopping data,identify customer behavior,reasoning out which items are popular,which goods are customers only interested in,which items are usually bought together,and which areas with large flow of people.Providing a scientific theory basis for sales promotion,and store layout,and then according to the preferences of customers to recommend related products,and provide customers with higher quality services.But due to a large number of tags moving at the same time,how to achieve accurate and effic ient data analysis of shopping data is a difficult problem.Many of the existing data analys is algorithms have the disadvantages of large delay and large energy consumption,which are not suitable for the shopping data analysis.This paper presents an analysis method in shopping data.Firstly,readers collect the phase of the passive RFID tag,and converted it into the relative moving speed of the product.Secondly,taking into account the interference between the RFID tags,the improved kNN algorithm(Improved k-Nearest Neighbor,I-kNN)is proposed based on this.Then,using the Hierarchical Agglomerative Clustering(HAC)algorithm,merging the items with similar speed.Finally,establishing the prototype of the proposed system,and the performance evaluations are carried out.The results show that our method is feasible in the practice of shopping data analysis,and time delay is significantly better than other algorithms.