计算机工程与应用2019，Vol.55Issue(2)：142-147,6.DOI:10.3778/j.issn.1002-8331.1710-0035
基于共享近邻的成对约束谱聚类算法
Pairwise Constrained Spectral Clustering Algorithm Based on Shared Nearest Neighborhood
摘要
Abstract
The spectral clustering algorithm is a machine learning algorithm based on the theory of spectral partitioning. It can cluster on any shape of the sample space and converge to the global optimal solution. However, the traditional spec-tral clustering algorithm is difficult to find out the large density difference clusters, the choice of parameters depends on multiple tests and personal experience. Combined with the idea of semi-supervised clustering, a pair of constrained spec-tral clustering algorithm based on shared neighbors(PCSC-SN)is proposed under the premise of giving some supervisory information. The PCSC-SN algorithm uses a shared neighbor to measure the similarity between data pairs, and uses the active constraint information to find the relationship between two data points. A series of experiments are done on the data set UCI. The experimental results show that this algorithm can obtain better clustering effect compared with the traditional clustering algorithm.关键词
半监督聚类/谱聚类/共享近邻/成对约束Key words
semi-supervised clustering/spectral clustering/shared neighbors/paired constraints分类
信息技术与安全科学引用本文复制引用
王小玉,丁世飞..基于共享近邻的成对约束谱聚类算法[J].计算机工程与应用,2019,55(2):142-147,6.基金项目
江苏省自然科学基金(No.BK20171142) （No.BK20171142）
江苏省产学研前瞻性联合研究项目(No.BY2016022-17/001). （No.BY2016022-17/001）
