一种改进人工蜂群的K-medoids聚类算法

针对传统K-medoids聚类算法初始聚类中心选择较敏感、聚类效率和精度较低、全局搜索能力较差以及传统蜂群算法初始蜂群和搜索步长随机选取等缺点，提出了一种基于粒子和最大最小距离法初始化蜂群和随着迭代次数增加动态调整搜索步长的人工蜂群算法，将改进的人工蜂群进一步优化K-medoids，以提高聚类算法的性能。实验结果表明：该算法降低了对噪声的敏感程度，具有较高的效率和准确率，较强的稳定性。

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.