Authors: Tingyu Ye; Jun Ye; Lei Wang
Addresses: School of Information Engineering, Nanchang Institute of Technology, Nanchang 330000, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang 330000, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang 330000, China
Abstract: The rough K-means clustering algorithm has a strong ability to deal with data with uncertain boundaries. However, this algorithm also has limitations such as sensitivity to initial data selection, as well as it use of fixed weights and thresholds, which results in unstable clustering results and decreased accuracy. In response to this problem, combined with the firefly algorithm, the original algorithm has been improved from three aspects. Firstly, based on the ratio of the number of objects in the dataset to the product of the difference of the objects in the dataset, a more reasonable method of dynamically adjusting the weights of approximation and boundary set is designed. Secondly, a method of adaptively realising the threshold associated with the number of iterations is given. Then, by constructing a new objective function, and take the objective function value as the firefly brightness intensity to perform the search and update iteration of the initial cluster centre point, the optimal solution obtained by each iteration of firefly is taken as the initial centre position of the algorithm. Experiment result shows that the new algorithm has improved the clustering effect.
Keywords: rough K-means algorithm; firefly algorithm; cluster centre; lower approximation and boundary set; objective function.
International Journal of Computing Science and Mathematics, 2023 Vol.17 No.1, pp.1 - 12
Received: 07 Nov 2020
Accepted: 12 Mar 2021
Published online: 20 Apr 2023 *