压缩近邻法是一种简单的非参数原型选择算法,其原型选取易受样本读取序列、异常样本等干扰.为克服上述问题,提出了一个基于局部均值与类全局信息的近邻原型选择方法.该方法既在原型选取过程中,充分利用了待学习样本在原型集中k个同异类近邻局部均值和类全局信息的知识,又设定原型集更新策略实现对原型集的动态更新.该方法不仅能较好克服读取序列、异常样本对原型选取的影响,降低了原型集规模,而且在保持高分类精度的同时,实现了对数据集的高压缩效应.图像识别及UCI(University of California Irvine)基准数据集实验结果表明,所提出算法集具有较比较算法更有效的分类性能.
针对传统K均值算法需要提前指定聚类数目且易陷入局部最优的问题,提出了一种采用万有引力定律自动确定类数的K均值算法(Gravity K均值算法,GK均值算法)。所提算法利用正交设计方法在数据空间均匀投放若干探测器,探测器根据万有引力定律移动,当两个探测器的距离小于给定阈值时合并为一个,当探测器处于稳定状态时,探测器的个数就是聚类的数目。将得到的探测器作为K均值算法的初始中心点,有效地避免了K均值算法陷入局部最优。实验结果表明:相比传统K均值算法,本文提出的方法可以自动确定聚类数目,并给出较好的初始中心,算法的迭代次数至少减少了25%,聚类正确率平均提高了14%,DB(Davies and Bouldin)聚类评价指标平均降低了0.19。
The similarity measure is crucial to the performance of spectral clustering. The Gaussian kernel function based on the Euclidean distance is usual y adopted as the similarity measure. However, the Euclidean distance measure cannot ful y reveal the complex distribution data, and the result of spectral clustering is very sensitive to the scaling parameter. To solve these problems, a new manifold distance measure and a novel simulated anneal-ing spectral clustering （SASC） algorithm based on the manifold distance measure are proposed. The simulated annealing based on genetic algorithm （SAGA）, characterized by its rapid convergence to the global optimum, is used to cluster the sample points in the spectral mapping space. The proposed algorithm can not only reflect local and global consistency better, but also reduce the sensitivity of spectral clustering to the kernel parameter, which improves the algorithm’s clustering performance. To efficiently apply the algorithm to image segmentation, the Nystrom method is used to reduce the computation complexity. Experimental results show that compared with traditional clustering algorithms and those popular spectral clustering algorithms, the proposed algorithm can achieve better clustering performances on several synthetic datasets, texture images and real images.
