A Kernelized Fuzzy C-means Clustering Algorithm based on Bat Algorithm

To overcome the defects of easily falling into local optimum and being sensitive to initial values brought by kernelized fuzzy means clustering algorithm (KFCM), a kernelized fuzzy means clustering algorithm based on bat algorithm (BA-KFCM) is proposed in this paper. In this paper, IRIS dataset, Glass dataset and Wine dataset in the classical datasets are used to simulate the experiment respectively, and the results of the algorithm are compared with those of the particle swarm optimization algorithm and the firefly algorithm so as to verify the effectiveness of the algorithm. The experimental results show that the proposed algorithm is superior to other algorithms in terms of effects and has a better quality of clustering.