一种邻域粒K均值聚类方法

K均值聚类,对于非凸、稀疏及模糊的非线性可分数据,其聚类效果不佳.针对此问题,通过引入粒计算理论,采用邻域粒化技术,提出一种邻域粒K均值聚类方法.样本在单特征上使用邻域粒化技术构造邻域粒子,在多特征上使用邻域粒化技术形成邻域粒向量;通过定义邻域粒与邻域粒向量的大小、度量和运算规则,提出两种邻域粒距离度量,并对所提出的邻域粒距离度量进行公理化证明.采用多个UCI数据集进行实验,将K均值聚类算法分别结合两种邻域粒距离度量,在邻域参数和距离度量两个方面与经典聚类算法进行比较,结果验证了所提出的邻域粒K均值聚类方法的可行性和有效性.

A neighborhood granular K-means clustering method

For non-convex, sparse and fuzzy nonlinear separable data, the clustering effect of K-means clustering is not good. Therefore, by introducing granule computing theory and using neighborhood granulation technology, a neighborhood granule K-means clustering method is proposed. The sample uses neighborhood granulation technology to construct neighborhood granules on a single feature, and to form neighborhood granule vectors on multiple features. By defining the size, measurement and operation rules of neighborhood granules and neighborhood granule vectors, two kinds of neighborhood granule distance measurements are proposed, and the axiomatic proof of the proposed neighborhood granule distance measurement is carried out. Finally, several UCI data sets are used to carry out experiments, the K-means clustering algorithm is combined with two neighborhood granule distance measurements respectively. It is compared with the classical clustering algorithm in two aspects of neighborhood parameters and distance measurement. The results show that the proposed neighborhood granular K-means clustering method is feasible and effective.