二阶段模糊聚类方法研究

Research on two-stage fuzzy clustering method

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，并将模拟退火算法引入模糊聚类，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 TFC, is presented. The first stage clusters data by a new clustering method. The second stage, the result of the first stage, is taken as the initial cluster centers, and the simulated annealing mechanism is induced 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 outlier in spectrum data.