Col. of Comp.& Inf. Eng., Zhejiang Gongshang Univ., Hangzhou, China

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Abstract—With China's rapid development of e-commerce and logistics, many large electronic business enterprises start to establish large volume warehouses. It takes a long time to distribute the goods every time, so the optimal distribution link can save a lot of time and it has important practical significance. In order to optimize goods inventory and delivery, the electronic commerce goods shopping cart stream data need to be analyzed. In this paper, a novel increment update clustering algorithm, named as IUCStream for commodity stream data analysis in ecommerce and logistics is proposed. In this algorithm, the correlation between goods is calculated and an efficient algorithm processing incremental updating of the data stream of goods is used to cluster different goods into groups. Finally, the algorithms’ superiority and effectiveness are verifying by an example.

Index Terms—Data stream, Clustering algorithm, Commodity correlation, e-commerce, logistics

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Cite: Peihua Fu, Yangfei Chen, and Hongbo Yin, " IUCStream: A Novel Increment Update Clustering Algorithm for Commodity Stream Data Analysis in e-Commerce and Logistics," Journal of Computers vol. 8, no. 3, pp. 795-802, 2013.