Aiming at the problem that LF ant colony clustering algorithm does not distinguish the importance of data set attributes, the algorithm efficiency is low and the clustering effect is unstable, this paper proposed a weighted global memory ant colony optimization(WGACO) based on entropy weight. Firstly, it calculated the entropy weight of each attribute by entropy weight method, and modified the Euclidean distance calculation formula to improve the clustering accuracy. It initialized the data object with the value of the largest weight attribute to enhance the stability of the clustering effect, and introduced the global memory matrix to reduce the invalid movement of ants and improve the efficiency of the algorithm. It added the convergence conditions of the algorithm to improve the practicability of the algorithm. Selecting seven real data sets and three artificially generated data sets in UCI database for numerical experiments, and compared WGACO with GMACO, SMACC and ILFACC three improved LF algorithms. The experimental results show that WGACO has a relatively good improvement in accuracy, algorithm efficiency and stability, and also has a good performance in processing high-dimensional data. Finally, WGACO has performed well in the segmentation of mall member users, reflecting its practical value.

[2023-04-26] Accepted Paper /doc/2023.02.0046/v1

[2023-10-05] Printed Article /doc/2023.02.0046/v2