基于改进粒计算的K-medoids聚类算法

针对传统K-medoids聚类算法对初始聚类中心敏感、收敛速度缓慢以及聚类精度不够高等缺点，提出一种基于改进粒计算、粒度迭代搜索策略和优化适应度函数的新算法。该算法利用粒计算思想在有效粒子中选择K个密度大且距离较远的粒子，选择其中心点作为K个聚类初始中心点；并在对应的K个有效粒子中进行中心点更新，来减少迭代次数；采用类间距离和类内距离优化适应度函数来提高聚类的精度。实验结果表明：该算法在UCI多个标准数据集中测试，在有效缩短迭代次数的同时提高了算法聚类准确率。

Improved K-medoids clustering algorithm based on improved granular computing

Due to the disadvantages such as sensitive to the initial selection of the center, slow convergent speed and poor accuracy in traditional K-medoids clustering algorithm, a novel K-medoids algorithm based on improved Granular Computing (GrC), granule iterative search strategy and a new fitness function was proposed in this paper. The algorithm selected K granules using the granular computing thinking in the high-density area which were far apart, selected its center point as the K initial cluster centers, and updated K center points in candidate granules to reduce the number of iterations. What's more, a new fitness function was presented based on between-class distance and within-class distance to improve clustering accuracy. Tested on a number of standard data sets in UCI, the experimental results show that this new algorithm reuduces the number of iterations effectively and improves the accuracy of clustering.