K-Means Clustering Algorithm Based on Improved Differential Evolution

The traditional K-means clustering algorithm has unstable clustering results and low efficiency due to the random selection of initial cluster centres.To address the limitations, an improved K-means clustering algorithm based on adaptive guided differential evolution (AGDE-KM) was proposed.First, adaptive operators were designed to enhance global search capability in the early stages and accelerate convergence in later stages.Second, a multi-mutation strategy with a weighted coefficient was introduced to leverage the advantages of different mutation strategies during various evolutionary phases, balancing global and local search capabilities and expediting convergence.Third, a Gaussian perturbation crossover operation was proposed based on the best individual in the current population, providing individuals with superior evolution directions while preserving population diversity across dimensions, thereby avoiding the local optima of the algorithm.The optimal solution output at the end of the algorithm implementation was used as the initial cluster centres, replacing the cluster centres randomly selected by the traditional K-means clustering algorithm.The proposed algorithm was evaluated on public datasets from the UCI repository, including Vowel, Iris, and Glass, as well as a synthetic dataset (Jcdx).The sum of squared errors (SSE) was reduced by 5.65%, 19.59%, 13.31%, and 6.1%, respectively, compared to traditional K-means.Additionally, clustering time was decreased by 83.03%, 81.33%, 77.47%, and 92.63%, respectively.Experimental results demonstrate that the proposed improved algorithm significantly enhances convergence speed and optimisation capability, significantly improving the clustering effectiveness, efficiency, and stability.