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Classification mining method of E-Commerce traffic big data based on clara algorithm
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DOI：10.1016/j.bdr.2026.100635.png)
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Abstract
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To achieve accurate and efficient classification of e-commerce traffic big data, a proposed method based on the Clara algorithm is used. The approach involves analyzing the components of e-commerce traffic big data in relation to the transaction process. The precision and time consumption of classifying and mining e-commerce traffic big data are significantly influenced by the effectiveness of data clustering. The Wavelet threshold function is utilized to de-noise and smoothen e-commerce traffic big data. This enhances the quality of traffic data. Subsequently, the Clara algorithm is employed to mine and cluster the de-noised traffic data. Finally, the support vector machine is used to classify the clustered data. The experimental results show that the method proposed in this paper performs clustering and classification mining on e-commerce traffic big data. The clustering results show no data overlap between categories, and the average accuracy of classification mining reaches 95.6%, with a maximum value of 97.9%. At the same time, the maximum single classification mining time is only 14.5 min, significantly improving the accuracy and efficiency of e-commerce traffic big data classification mining.
Keywords:
Clara algorithm
E-commerce traffic big data
Classification mining
Support vector machine
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