一种改进的粗k 均值聚类算法

Lingras提出的粗K均值聚类算法易受随机初始聚类中心和离群点的影响,可能出现一致性和无法收敛的聚类结果.对此,提出一种改进的粗K均值算法,选择潜能最大的K个对象作为初始的聚类中心,根据数据对象与聚类中心的相对距离来确定其上下近似归属,使边界区域的划分更合理.定义了广义分类正确率,该指标同时考虑了下近似集和边界区域中的对象,评价算法性能更准确.仿真实验结果表明,该算法分类正确率高,收敛速度快,能够克服离群点的不利影响.

An improved rough k-means clustering algorithm

Rough k-means clustering algorithm proposed by Lingras is sensitive to the initial centers of the k cluster and
outliers and may result in identical clustering and non-convergence. In this paper, an improved rough k-means clustering
algorithm is proposed. The k objects with maximum potentials are chosen as initial centers. The absolute distance between
object and center of clusters is considered to decide whether a data object belongs to the lower or upper approximation set
of a cluster, so the division of boundary area is more reasonable. General classification accuracy considering the objects in
lower approximation set and boundary area is defined for rough k-means clustering algorithm, and it is more appropriate for
evaluating rough k means clustering. The simulation results show that, the proposed algorithm has the advantages of high
classification accuracy and fast convergence, and can also avoid the bad influence of outlier.