摘要
在海量的天体光谱数据中利用无监督聚类学习方法将天体自动分类具有更加诱人的前景。针对当前聚类方法存在的缺点,提出一种高效的高维数据硬划分算法,在此基础上提出了一种分阶段模糊聚类方法。第一阶段,利用硬划分算法对数据聚类,克服了模糊聚类算法对初始值敏感的缺点。第二阶段,以第一阶段运算结果作为初始值,进行模糊聚类的,并将微粒群算法引入模糊聚类,从而保证了聚类结果的全局最优性。实验结果表明,该方法用于天体光谱聚类是可行的、有价值的。
A novel high-dimensional clustering algorithm is proposed. On the basis of this, a two-stage fuzzy clustering approach, named TSPFCM, is presented. On the first stage, data is clustered by a new clustering method. On the second stage, the result of the first stage is taken as the initial cluster centers, and PSO mechanism is inducted into fuzzy clustering to solve the locality and the sensitiveness of the initial condition of Fuzzy C-means Clustering. The running results of the system show that it is feasible and valuable to apply this method to mining the clustering in spectrum data.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2009年第4期1137-1141,共5页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金项目(70573075)
山西省青年基金项目(2008021028)资助
关键词
模糊聚类
微粒群
恒星光谱数据
全局最优
Fuzzy clustering, Particle swarm optimization approach
Star optical spectrum data, Global optimization
