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User Behavior Data Analysis of Taobao Online Based on Flink-Based K-Means Algorithm

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2020 International Conference on Applications and Techniques in Cyber Intelligence (ATCI 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1244))

Abstract

On the k-value sensitive effect of K-means algorithm in data clustering, based on the characteristics of density distribution, the kernel density selection scheme is adopted to improve the algorithm. For massive data, the improved algorithm is parallelized based on Flink platform. In the practical application of mobile e-commerce, experiments are repeated in serial mode and parallel mode respectively, and the improved algorithm and K-means algorithm are used for specific comparison. The results show that the improved algorithm will also have good clustering effect in real life. It can be seen that the improved algorithm not only has accuracy and efficiency, but also has practical significance.

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Acknowledgements

This study was funded by Anhui Provincial Education Department Natural Science Research Key Project (KJ2018A0352); Fuyang Normal University Humanities and Social Sciences Research Project (2018FSSK05ZD). And we would like to thank the re-viewers for their beneficial comments and suggestions, which improves the paper.

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Authors and Affiliations

  1. Fuyang Normal University, Fuyang, 236037, Anhui, China

    Kunpeng Cai & Lijuan Ma

Authors
  1. Kunpeng Cai
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  2. Lijuan Ma
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Corresponding author

Correspondence to Kunpeng Cai .

Editor information

Editors and Affiliations

  1. Distributed System and Security Research Cluster, Faculty of Science, Engineering and Built Environment, Deakin University, Geelong, VIC, Australia

    Jemal H. Abawajy

  2. Department of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, TX, USA

    Kim-Kwang Raymond Choo

  3. Shanghai University of Medicine and Health Sciences, Shanghai, China

    Zheng Xu

  4. University of Oklahoma, Norman, OK, USA

    Mohammed Atiquzzaman

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© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

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Cai, K., Ma, L. (2021). User Behavior Data Analysis of Taobao Online Based on Flink-Based K-Means Algorithm. In: Abawajy, J., Choo, KK., Xu, Z., Atiquzzaman, M. (eds) 2020 International Conference on Applications and Techniques in Cyber Intelligence. ATCI 2020. Advances in Intelligent Systems and Computing, vol 1244. Springer, Cham. https://doi.org/10.1007/978-3-030-53980-1_125

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