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The traditional clustering method of online shopping user group visit neglects the extraction of user voice access features, which results in poor clustering effect. In order to improve the efficiency… Click to show full abstract
The traditional clustering method of online shopping user group visit neglects the extraction of user voice access features, which results in poor clustering effect. In order to improve the efficiency of online shopping, this paper constructs a cluster model of online shopping user access based on data mining algorithm of electronic communication. Finally, a weighted fuzzy C-means algorithm based on K-means is used to realize the access clustering of online shopping user groups. The results show that the F1 value of clustering model is higher than that of K mean, PAM and Clara model, which proves the application effect of the model.
Journal Title: International Journal of Speech Technology
Year Published: 2021
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