摘要
提出一种尺度参数与初始中心自适应的谱聚类算法。与传统谱聚类算法中使用全局尺度参数相比,本算法根据样本数据点邻域信息自适应确定的局部尺度参数,充分考虑了数据的内在结构,并且将粒子群优化的FCM算法引入算法中,寻找最优粒子作为初始中心,解决初始聚类中心敏感性问题。实验结果表明,文章提出的算法相比原始谱聚类算法,聚类结果更稳定,正确率更高。
This paper presents an improved spectral clustering algorithm where scale parameters and initial centers are adaptiv- ely determined. Comparing with the traditional spectral clustering algorithm using globe scale parameter, the algorithm deter- mines the local scale parameters according to the neighborhood information of sample data adaptively, which fully considers in- herent structure of the sample data. And the particle swarm optimized FCM algorithm is introduced into the algorithm to solve the sensitive problem of initial cluster centers by finding the optimum swarm. Experimental results show that the clustering re- sults of proposed algorithm have more stable and higher accuracy rate compared with the original spectral clustering.
出处
《信息通信》
2013年第7期20-22,共3页
Information & Communications
关键词
谱聚类
尺度参数
自适应
粒子群优化
spectral clustering, scale parameter, adaptive, particle swarm optimized
