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Fast clustering algorithm of commodity association big data sparse network

Author

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  • Hailan Pan

    (Shanghai University
    Shanghai Polytechnic University
    Shanghai Polytechnic University)

  • Xiaohuan Yang

    (Cosco Shipping Technology CO)

Abstract

How to dig out the business perspectives and market rules behind commodity transaction data, explore the relationship between commodities, so as to more scientifically and rationally classify and promote commodity categories and improve commodity sales performance for e-commerce companies has become a recent research hotspot. To this end, this paper proposes to use clustering algorithm to explore the hidden laws of commodity-related big data. This article first consults a large amount of information through the literature survey method, systematically summarizes the relevant theoretical knowledge of the association rule method and clustering algorithm and gives a detailed introduction to its application in the commodity association big data mining. The research in this area has laid a sufficient theoretical foundation; after that, the Apriori algorithm in the association rules and the K-means algorithm in the clustering algorithm were used to carry out the fast clustering algorithm experiment of the commodity-related big data sparse network and the commodity transaction data was introduced in detail. The process of association analysis and cluster analysis; then taking China’s well-known e-commerce platform Jingdong Mall as an example, by investigating the commodity transaction records of Jingdong Mall in the 4th week of July, the association and cluster analysis of its commodity transaction data were found. Among them, mobile phones and Bluetooth earphone, laptops and Bluetooth earphone, laptops and hard disks have the highest correlation and their confidence thresholds have reached 25%, 35 and 40% respectively. Finally, when the clustering results were tested, they were also found in the store. Strengthening the push and shopping guide of highly relevant product combinations on the website pages will increase the sales of products.

Suggested Citation

  • Hailan Pan & Xiaohuan Yang, 2021. "Fast clustering algorithm of commodity association big data sparse network," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(4), pages 667-674, August.
    • Handle: RePEc:spr:ijsaem:v:12:y:2021:i:4:d:10.1007_s13198-021-01060-8
    DOI: 10.1007/s13198-021-01060-8
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    References listed on IDEAS

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    1. Zoey Yi Zhao & Meng Xie & Mike West, 2016. "Dynamic dependence networks: Financial time series forecasting and portfolio decisions," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 32(3), pages 311-332, May.
    2. Barbaglia, Luca & Wilms, Ines & Croux, Christophe, 2016. "Commodity dynamics: A sparse multi-class approach," Energy Economics, Elsevier, vol. 60(C), pages 62-72.
    3. Zoey Zhao & Meng Xie & Mike West, 2016. "Rejoinder to ‘Dynamic dependence networks: Financial time series forecasting and portfolio decisions’," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 32(3), pages 336-339, May.
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