关键词: 聚类算法, K-means聚类, 三支决策, 萤火虫算法, 变异策略

Abstract:

To address problems such as the premature phenomenon in the three-way clustering algorithm caused by the random selection of initial cluster centers and the need for repeated experiments to determine the value of q in the q-nearest neighbor concept, a three-way clustering algorithm optimized by a variant of the firefly algorithm is proposed. The firefly algorithm is employed to solve the problem of sensitivity to initial cluster centers. The target function value is taken as the brightness intensity of firefly to search the clustering center point, and the optimal solution is taken as the clustering center of the algorithm for iteration. The boundary domain attribution formula and adaptive threshold value are proposed, so that the samples in the boundary domain can be divided into the core domain as far as possible if they meet the threshold condition, avoiding the problem of too many boundary domain samples. The experimental results on the UCI datasets show that the improved algorithm significantly reduces the number of iterations, improves the accuracy of the clustering results, and verifies the stability and effectiveness of the algorithm。

Key words: clustering algorithm, K-means clustering, three-way decision clustering, firefly algorithm, mutation strategy