Title: Data mining research on sustainable business model innovation of enterprises based on particle swarm algorithm

Authors: Jianxiong Hu

Addresses: School of Economics, Tongling University, Tongling, 244061, China

Abstract: To achieve fast and accurate data mining, this research proposes a data mining method based on particle swarm optimisation algorithm, which first introduces a time factor to optimise the fractional-order particle swarm algorithm (TFFV-PSO), and then implements automatic clustering on the basis of improved fractional-order PSO. In the result part, when searching in the early stage, the TFFV-PSO proposed in this paper can avoid forming local optimisation in multimodal function, and has better robustness. The convergence speed of the TFFV-PSO algorithm is faster and more accurate in the two-dimensional case than in the ten-dimensional case. The number of clusters of the new algorithm in different datasets is consistent with the actual, and the correct rates in dataset 1 and dataset 2 can reach the algorithm's average DBI values are lower. The average DBI values of the algorithm are lower than those of other algorithms.

Keywords: particle swarm algorithm; enterprise; sustainability; business model creation; data mining.

DOI: 10.1504/IJCSYSE.2023.132912

International Journal of Computational Systems Engineering, 2023 Vol.7 No.2/3/4, pp.106 - 114

Received: 24 Nov 2022
Accepted: 15 Feb 2023
Published online: 16 Aug 2023 *

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