一种具有方向特点的网络对象聚类算法

A Clustering Algorithm for Network Objects with Direction Factors

对分别采用欧氏距离和网络距离作为相似性测度的聚类方法进行分析,并从空间网络中对象间着手,提出一种具有方向特点的网络对象聚类算法.算法利用空间网络的邻接关系,将两种距离结合起来作为聚类的相似性测度以提高聚类的精度.算法分析和实验证明,该算法的聚类效果优于单一度量的聚类方法.

Clustering methods are analyzed in which Euclidean distance and network distance are used as a similarity measure respectively. The neighbor correlation between objects on a spatial network is discussed and a clustering algorithm is proposed for network objects with consideration of direction factors. The algorithm combines the two distances as the similarity measure of clustering by using the neighbor correlation. The analysis and experimental results indicate that the effectiveness of the proposed algorithm is better than those only using one measure.