机构地区: 河北经贸大学
出 处: 《计算机科学》 2010年第2期209-211,共3页
摘 要: 传统的K-means算法对初始聚类中心非常敏感,聚类结果随不同的初始输入而波动,算法的稳定性下降。针对这个问题,提出了一种优化初始聚类中心的新算法:在数据对象的模糊粒度空间上给定一个归一化的距离函数,用此函数对所有距离小于粒度d_λ的数据对象进行初始聚类,对初始聚类簇计算其中心,得到一组优化的聚类初始值。实验对比证明,新算法有效地消除了传统K-means算法对初始输入的敏感性,提高了算法的稳定性和准确率。 The traditional K-means is very sensitive to initial clustering centers and the clustering result will wave with the different initial input. To remove this sensitivity, a new method was proposed to get initial clustering centers. This method is as follows: provide a normalized dis'tahoe function in the fuzzy granularity space of data objects,then use the function to do a initial clustering work to these data obiects who has a less distance than granularity da, then get the initial clustering centers. The test shows this method has such advantages on increasing the rate of accuracy and reducing the program times.
领 域: [自动化与计算机技术] [自动化与计算机技术]