Clustering Algorithm Based on Artificial Bee Colony Optimization

After analyzing the disadvantages of sensitivity to the initial selection of the center, low clustering accuracy and the poor global search ability of k-medoids clustering algorithm, a clustering algorithm based on improved artificial bee colony (ABC) is proposed. By improving the initialization of bee colony, adjusting the search step dynamically with iteration increasing, and then introducing the selection probability based on sorting instead of depending on fitness directly, the ABC algorithm will quickly converge to global optimal. This paper will further optimize k-medoids to improve the performance of the clustering algorithm. The experimental results show that this algorithm can reduce the sensitive degree of the initial center selection and the noise, has high accuracy and strong stability.