Article: K-means clustering algorithm for data distribution in cloud computing environment Journal: International Journal of Grid and Utility Computing (IJGUC) 2021 Vol.12 No.3 pp.322 - 331 Abstract: This study analyses the data structure in cluster analysis. It is a clustering method that randomly selects a known number of points and then continues to expand. Through the comparative experiments on the clustering accuracy of different similarity matrices, the experimental analysis on the effectiveness of the model, the distribution of e-commerce data under cloud computing and the calculation time of different clustering algorithms, we can better understand the K-means clustering algorithm and the status of e-commerce in cloud computing environment. The experimental results show that if the appropriate similarity function is selected, the result of spectral clustering is usually not lower than that of simple K-means clustering. When the number of users reaches 4000, the list reading time of the K-means clustering algorithm is 3.15 s, while the other three algorithms consume more time. Inderscience Publishers - linking academia, business and industry through research

Title: K-means clustering algorithm for data distribution in cloud computing environment

Authors: Hailan Pan; Yongmei Lei; Shi Yin

Addresses: School of Computer Engineering and Science, Shanghai University, Baoshan, Shanghai 200444, China; Research Centre of Resource Recycling Science and Engineering, Shanghai Polytechnic University, Pudong, Shanghai 201209, China ' School of Computer Engineering and Science, Shanghai University, Baoshan, Shanghai 200444, China ' Research Centre of Resource Recycling Science and Engineering, Shanghai Polytechnic University, Pudong, Shanghai 201209, China; School of Economics and Management, Shanghai Polytechnic University, Pudong, Shanghai 201029, China

Abstract: This study analyses the data structure in cluster analysis. It is a clustering method that randomly selects a known number of points and then continues to expand. Through the comparative experiments on the clustering accuracy of different similarity matrices, the experimental analysis on the effectiveness of the model, the distribution of e-commerce data under cloud computing and the calculation time of different clustering algorithms, we can better understand the K-means clustering algorithm and the status of e-commerce in cloud computing environment. The experimental results show that if the appropriate similarity function is selected, the result of spectral clustering is usually not lower than that of simple K-means clustering. When the number of users reaches 4000, the list reading time of the K-means clustering algorithm is 3.15 s, while the other three algorithms consume more time.

Keywords: cloud computing; data distribution oriented; K-means clustering algorithm; e-commerce platform; data structure; data distribution.

DOI: 10.1504/IJGUC.2021.117873

International Journal of Grid and Utility Computing, 2021 Vol.12 No.3, pp.322 - 331

Received: 24 Aug 2020
Accepted: 23 Oct 2020
Published online: 04 Oct 2021 *

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Title: K-means clustering algorithm for data distribution in cloud computing environment

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