K-medoids clustering algorithm based on improved Artificial Bee Colony

Due to the disadvantages such as sensitivity to the initial selection of the center, low clustering efficiency and accuracy and the poor global search ability in traditional K-medoids clustering algorithm, and the random selection of initial swarm and search step in traditional colony algorithm and so on, this paper proposes a new Artificial Bee Colony algorithm in which the initialization of bee colony is based on granules and maximum minimum distance method and the adjustment of search step is dynamic with iteration number increasing. This paper will further optimize K-medoids to improve the performance of the clustering algorithm. The results of experiments show that this algorithm can reduce the sensitive degree of the noise, has high accuracy and efficiency, strong stability.