中文说明：应用背景 模糊c-均值聚类算法 fuzzy c-means algorithm (FCMA)或称（ FCM）。在众多模糊聚类算法中，模糊C-均值（ FCM） 算法应用最广泛且较成功，它通过优化目标函数得到每个样本点对所有类中心的隶属度，从而决定样本点的类属以达到自动对样本数据进行分类的目的。 关键技术 模糊聚类分析作为无监督机器学习的主要技术之一，是用模糊理论对重要数据分析和建模的方法，建立了样本类属的不确定性描述，能比较客观地反映现实世界，它已经有效地应用在大规模数据分析、数据挖掘、矢量量化、图像分割、模式识别等领域，具有重要的理论与实际应用价值，随着应用的深入发展，模糊聚类算法的研究不断丰富。
English Description：
Application backgroundC-means algorithm fuzzy (FCMA) or c- is also called (FCM). In many fuzzy clustering algorithm, fuzzy C-means (FCM) algorithm is used as the most widely used and successful, it is through the optimization objective function of each sample point on all classes of membership degree, so as to determine the sample points of the category to achieve automatic classification of the sample data.Key TechnologyFuzzy clustering analysis as one of the main techniques for supervised machine learning, fuzzy theory of important data analysis and modeling methods, the establishment of a sample of the uncertainty description, can objectively reflect the real world, it has effective application in large-scale data analysis, data mining, vector quantization, image segmentation, pattern recognition, has the important theory significance and practical value. With the further development of the application of fuzzy clustering algorithm continue