基于改进引力搜索算法的K-means聚类

针对K-means算法的聚类结果极易受到聚类中心的影响而陷入局部最优解的问题，提出一种基于改进引力搜索的K-means聚类算法。首先引入自适应概念，对引力系数衰减因子进行控制，提高算法的全局探索能力和局部开发能力；然后，引入免疫克隆选择机制，以便算法能够有效跳出局部最优，并通过对12个基准测试函数的实验验证改进引力搜索算法的有效性和优越性；最后，通过结合改进的引力搜索算法和K-means算法，提出一种新的聚类算法A2F-GSA-Kmeans，并在6个测试数据集上的实验表明，该算法具有较好的聚类质量。

Novel K-means clustering algorithm based on improved gravitational search algorithm

In order to solve the problem that the clustering result of K-means algorithm gets affected by the initial cluster centers easily, this paper proposed a novel K-means clustering algorithm based on improved gravitational search algorithm. Firstly, it enhanced the global exploration and local exploitation capability of the algorithm with the introduction of adaptive concept to control the attenuation factor of gravitational constant. Then, it introduced immune clonal selection algorithm to make the algorithm jump out of the local optimum efficiently. The experimental results on twelve test functions prove the effectiveness and superiority of the improved GSA. Finally, by combining the improved GSA with K-means algorithm, this paper proposed a new clustering algorithm called A2F-GSA-Kmeans. The experimental results on six test datasets show that the algorithm has better clustering quality.