E-commerce return data based on frequent itemset mining and time series symbolization clustering

Abstract

The rapid development of the Internet economy has made the e-commerce sales model more mature, but e-commerce returns are more and more frequent, resulting in a large number of returns data. However, traditional data mining methods have not taken into account the impact of time series on data, making it difficult to accurately and effectively analyze e-commerce return data. In order to effectively handle the time series characteristics in e-commerce return data, research is conducted on time series symbolization of raw data, converting continuous numerical time series data into discrete symbol series. In order to optimize the linear segmentation effect of time series, an incremental error segmentation method is introduced to replace the traditional sliding window segmentation method. The segmentation points are dynamically adjusted by gradually calculating the fitting error of each time period. At the same time, K-means clustering algorithm is used to cluster symbol sequences, and a frequent pattern growth algorithm is introduced for clustering symbol sequence frequent itemset mining. The results showed that the incremental error segmentation method used in the study reduced the fitting error by an average of 1.382 when the compression rate exceeded 90%. Under the same support rate, the proposed algorithm only consumed about 200 MB of memory and ran for only 60 seconds, proving the effectiveness and accuracy of the research method. Meanwhile, the analysis results of the example showed that an increase in merchant sales, a decrease in logistics anomalies, and a decline in store reputation all had a significant impact on product returns. This indicates that the results of this study can help businesses understand return trends, optimize sales and logistics, and thereby reduce return rates.

Keywords

K-means clustering FP-growth frequent itemsets time series data mining internet

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