K-means clustering algorithm for data distribution in cloud computing environment. (24th September 2021) - Pan, Hailan; Lei, Yongmei; Yin, Shi
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.