The fuzzy c-means algorithm (FCM) is one of widely used clustering algorithms.
模糊c均值算法(FCM)是经常使用的聚类算法之一。
An improved color segmentation algorithm is presented based on weighting fuzzy c-means (FCM) clustering algorithm.
在加权模糊c -均值(FCM)聚类算法的基础上，对分色算法进行了改进。
This paper discusses the fuzzy C-means algorithm (FCM), one of the fuzzy clustering methods and clustering validity measurements.
本文讨论了模糊聚类中的模糊C均值算法和聚类有效性测度。
Fuzzy c-clustering algorithm (FCM) is a useful tool in edge detection of digital image.
模糊聚类算法(FCM)应用于数字图像的边缘检测已取得了较好的效果。
A modified FCM algorithm for fire image segmentation is proposed in this paper based on the study of fuzzy clustering algorithm and characteristics of fire images.
本文在研究模糊聚类算法和火灾图像特点的基础上，提出了一种基于改进FCM算法的彩色火灾图像分割方法。
Then, expounds the theory of fuzzy set and fuzzy clustering, goes into details for the classical FCM algorithm, with a analysis for its performance and shortcomings.
接着阐述了模糊理论与模糊聚类的相关内容，详细介绍了经典的FCM算法，分析了它的性能及缺点。
It is a procedure of the label following an unsupervised fuzzy clustering that fuzzy c-means (FCM) algorithm is applied to image segmentation.
算法用于图像分割是一种非监督模糊聚类后再标定的过程。
The proposed fuzzy clustering algorithm incorporates the discriminating vector into its update equations such that the obtained update equations do not take commonly-used FCM-like forms.
该算法将鉴别矢量引入迭代更新方程，因此其异于常见的FCM聚类方程形式。
The proposed fuzzy clustering algorithm incorporates the discriminating vector into its update equations such that the obtained update equations do not take commonly-used FCM-like forms.
该算法将鉴别矢量引入迭代更新方程，因此其异于常见的FCM聚类方程形式。
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