计算机技术与发展2017，Vol.27Issue(11)：19-23,5.DOI:10.3969/j.issn.1673-629X.2017.11.004
基于自然邻的自适应谱聚类算法
An Adaptive Spectral Clustering Algorithm Based on Natural Neighbor
摘要
Abstract
In traditional spectral clustering algorithm,the input scaling parameters are needed to construct the similar matrix. In addition, the exact number of clusters is needed to be input in the subsequent k-means process. The above two parameters have a huge influence on the clustering effect. Aiming at the above problems,an adaptive spectral clustering algorithm based on natural neighbors is proposed. It does not need to input any parameters artificially and can achieve complete self-adaptation,the main way of which is to obtain the prox-imity information between the points by the natural neighbor algorithm, including the number of natural neighbors and inverse natural neighbors,the natural neighbor sets and the inverse natural neighbor sets. Through the case analysis,in the multi-scale or popular data set,the above priori information is made full use of to construct a similarity matrix more consistent with the actual situation. In addition, the number of clusters is gained according to the idea of spread of neighbors. The algorithm is applied to some artificial data sets,and compared with the spectral clustering algorithm,improving the clustering effect remarkably. Experimental results show that it has certain validity and superiority.关键词
谱聚类/自然邻/自适应/尺度参数/聚类数目Key words
spectral clustering/natural neighbor/adaptive/scaling parameter/number of clustering分类
信息技术与安全科学引用本文复制引用
朱庆生,付飘飘,张程..基于自然邻的自适应谱聚类算法[J].计算机技术与发展,2017,27(11):19-23,5.基金项目
重庆市基础与前沿研究计划项目(cstc2013jcyjA40049) （cstc2013jcyjA40049）
