密度敏感的距离测度在特定图像聚类中的应用  

Application of Density-sensitive Distance Measure in Special Image Clustering

作　　者：吴毓龙[1] 袁平波[1] 

机构地区：[1]中国科学技术大学电子工程与信息科学系,合肥230027

出　　处：《计算机工程》2009年第6期210-212,共3页Computer Engineering

基　　金：多媒体计算与通信教育部微软重点实验室开放基金资助项目(06120809)

摘　　要：根据聚类假设,提出一种新的基于图的半监督学习算法,称为密度敏感的半监督聚类。该算法引入一种密度敏感的距离测度,它能较好地反映聚类假设,并且充分挖掘了数据集中复杂的内在结构信息,同时与基于图的半监督学习方法相结合,使得算法在聚类性能上有了显著的提高。经过实验仿真进一步表明,该算法在特定图像应用上具有优越性。Based on clustering assumption, this paper proposes a novel semi-supervised learning algorithm based on graph, named Density- sensitive Semi-supervised Clustering（DS-SC）. The approach introduces a density-sensitive distance measure which reflects the clustering assumption well and fully exploits the competitive inherent structure information among dataset, and combining it with a graph-based semi-supervised learning methods leads to prominent clustering performance enhance of DS-SC. The results demonstrate the superiority of DS-SC in the application of a specific image in the further simulation.

关 键 词：半监督聚类 密度敏感 聚类假设 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]