摘要
由于传统模糊C均值聚类算法存在缺陷,该文给出了一种结合加权模糊C均值聚类与聚类有效性指数的算法。利用数据点的密度大小作为权值,借助数据本身的分布特性,该方法不仅在一定程度上克服了模糊均值算法的缺陷——有对数据集进行等划分的趋势,而且具有良好的收敛性。
As the fuzzy C-means clustering algorithm(FCM) has shortcomings. To solve the above problem, this paper proposes a combination of WFCM algorithm and cluster validity index. Distributing density size of data dot is regard as weighted value, in virtue of distributing characteristic of data's own, the method has not only to certain extent overcome limitation of fuzzy C-means algorithm--having limitation of equal partition trend for data sets, but also been favorable convergence.
出处
《杭州电子科技大学学报(自然科学版)》
2012年第4期99-101,共3页
Journal of Hangzhou Dianzi University:Natural Sciences
关键词
模糊均值
点密度函数
遥感图像
聚类
有效性指数
fuzzy means
dot density function
remote sensing image
clustering
validity index