Results Fuzzy K means clustering algorithm can segment white matter, gray matter and CSF better from the MR head images.
结果模糊K- 均值聚类算法能很好地分割出磁共振颅脑图像中的灰质、 白质和脑脊液。
According to the characteristics of traffic flow, it USES fuzzy C-means clustering algorithm to deal with these fuzzy factors.
根据交通流特性，运用模糊C均值聚类算法对交通流各要素进行模糊分析处理。
The results revealed that fuzzy c-means clustering algorithm could be used to delineate management zones by using the given variables.
利用所选取的变量，模糊c均值聚类算法可以较好地进行管理分区划分。
Based on the traditional fuzzy C-means clustering algorithm, a new fuzzy C-means clustering algorithm for interval data clustering is proposed.
在传统模糊c -均值聚类算法的基础上，提出了一种新型区间值数据模糊聚类算法。
The improved fuzzy C-means clustering algorithm has better robustness and makes the cluster results insensitive to the predefined cluster number.
改进后的模糊C-均值聚类算法具有更好的鲁棒性，且放松了隶属度条件，使得最终聚类结果对预先确定的聚类数目不敏感。
The classical C-means clustering algorithm (CMA) is a well-known clustering method to partition an image into homogeneous regions.
经典的C -均值聚类算法（CMA）是将图像分割成C类的常用方法，但依赖于初始聚类中心的选择。
By means of analyzing kernel clustering algorithm and rough set theory, a novel clustering algorithm, rough kernel k-means clustering algorithm, was proposed for clustering analysis.
通过研究核聚类算法，以及粗糙集，提出了一个新的用于聚类分析的粗糙核聚类方法。
According to the characters of the images, the algorithm separated image into several regions by K-means clustering algorithm, and each region is equalized respectively within their gray levels.
该算法基于图像的特点，利用K均值聚类算法将图像分成几个灰度区间，然后再分别进行均衡化。
And this paper also improved the initial center point's selection of K-Means clustering algorithm.
另对聚类算法初始聚类中心的选取也做了改进。
And this paper also improved the initial center point's selection of K-Means clustering algorithm.
另对聚类算法初始聚类中心的选取也做了改进。
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