关键词: 时频识别（RFID）, 相位, 速度, 改进k邻近算法, 动态时间规划, 层次聚类算法

Abstract: Aiming at the problem that it is difficult for the store to analyze the customer’s shopping process, a method for deep shopping data analysis is proposed. By using the phase information that reader collects from passive RFID tags to calculate the relative speed of items, then the shopping process is analyzed according to the relative movement speed. Considering the dense distribution characteristics of shopping malls, an improved I-kNN algorithm is proposed, and then I-kNN and HAC are used for in-depth analysis of the speed data to identify products of interest, hot items, hot areas and related commodities. Finally, the prototype of the proposed system is established by using the existing commercial equipment, and has been tested in the actual environment. Theoretical analysis and experimental results show that the algorithm is feasible in the analysis of shopping data, the accuracy rate is more than 78%. Meanwhile, the search data quantity and the computational complexity are reduced.

Key words: Radio Frequency Identification（RFID）, phase, velocity, improved k-nearest neighbor, dynamic time warping, hierarchical agglomerative clustering