一种增量式MinMax k-Means聚类算法

针对MinMax k-means算法易产生空解、 收敛速度慢和计算效率低的问题, 提出一种增量式MinMax k-means聚类算法. 该算法从给定的初始聚类个数开始, 以固定步长递增式产生新的聚类中心, 采用基于数据均衡的快速分裂方法产生增量聚类中心, 从而避免了传统增量聚类中心选择中遍历数据、k-means聚类算法运行次数过多导致的大计算量问题. 与MinMax k-means及相关算法的对比实验结果表明, 该算法在计算效率和求解精度上均优于对比算法, 有效改善了MinMax k-means聚类对初始化中心敏感和易产生空解的问题.

An Incremental MinMax k-Means Clustering Algorithm

Aiming at the problem that MinMax k-means algorithm was easy to generate empty solutions, slow convergence speed and low computational efficiency, we proposed an incremental MinMax k-means clustering algorithm. The algorithm started from a given initial clustering number, and generated new cluster centers by increasing a fixed step length. The fast dividing method based on data balance was used to generate incremental cluster centers, so as to avoid the large amount of calculation problem caused by traversing data and too many running times of k-means clustering algorithm in traditional incremental clustering center selection. Compared with MinMax k-means and related algorithms, the experimental results show that the algorithm is superior to the comparison algorithm in calculation efficiency and accuracy, and effectively improves the sensitivity of MinMax k-means clustering to initialization center and easy to generate empty solutions.