硬/软分类:C均值聚类是一种硬聚类方法,每个数据点只能属于一个群集;而模糊聚类是一种软聚类方法,允许数据点以不同程度属于不同的群集。 算法复杂度:C均值聚类通常比模糊聚类算法简单,因为它只需要计算每个数据点到群集中心的距离,而不需要处理隶属度的模糊性。 对异常值的敏感度:模糊聚类对异常值的影响较小,而C...
Abu-Zanona, "Fuzzy C-Mean Clustering Algorithm Modification and Adaptation for Applications", World of Computer Science and Information Technology Journal (WCSIT), ISSN: 2221-0741, Vol. 2, No. 1, 42-45, 2012Bassam M. El-Zaghmouri, Marwan A. Abu-Zanona" Fuzzy C-Mean Clustering Algorithm ...
Fuzzy c-means(FCM) is a data clustering technique where each data point belongs to a cluster to a degree that is specified by a membership grade. The FCM algorithm starts with an initial guess for the cluster centers, which represent the mean location of each cluster. The initial guess for...
To improve the accuracy of text clustering,fuzzy c-means clustering based on topic concept sub-space(TCS2FCM) is introduced for classifying texts. 为了改善文本聚类的准确度,提出用基于主题概念子空间的模糊c-均值聚类(TCS2FCM)方法来分类文本。3...
模糊C-均值聚类算法(FCM)已广泛地运用到MR图像的分割中。 3)C-Mean Fuzzy Clustering模糊C-均值聚类算法 1.An algorithm for selecting episode representation frames by using an approach of key frame extraction based on multiple characters and C-Mean fuzzy clustering is detailed in the paper.该文在子...
模糊C-均值聚类算法 1. An algorithm for selecting episode representation frames by using an approach of key frame extraction based on multiple characters and C-Mean fuzzy clustering is detailed in the paper. 该文在子镜头的关键帧提取方法基础上,利用模糊C-均值聚类算法,实现了一种基于子镜头聚类的情节...
Fuzzy c-means clustering The fuzzy c-means is a fuzzy variant of the k-means partitional algorithm. With fuzzy c-means, the centroid (also called a prototype) of a cluster is computed as being the mean of all examples, weighted by their degree (uk) of belonging to the cluster Ck: (12...
改进Fuzzy C-means 算法 Fuzzy C-means算法概述 Fuzzy C-means算法是聚类算法中主要算法之一,它是一种基于划分的聚类算法,是最为经典的,同时也是使用最为广泛的一种基于划分的聚类算法,它属于基于距离的聚类算法。1967年,J.B.MacQueen提出的Fuzzy C-means算法是目前为止在工业和科学应用中一种极有影响的聚类技术...
本文就将采用改进Fuzzy C-means算法对基于用户特征的微博数据进行聚类分析。 去年,我们为一位客户进行了短暂的咨询工作,他正在构建一个主要基于微博用户特征聚类研究的分析应用程序。首先对聚类分析作系统介绍。其次对改进Fuzzy C-means算法进行文献回顾,对其概况、基本思想、算法进行详细介绍,再是应用了改进Fuzzy C-means...
模糊C均值算法（FCM）的目标函数为：模糊参数m ＞ 1决定聚类模糊度，大多数情况下m = 2。当目标函数达到最小值时，结果最优。聚类中心的计算基于隶属度。模糊聚类问题转换为有约束条件的最小值问题，其最优值的求解需通过迭代过程获得。终止迭代条件为U( t) － U( t － 1) <[公式]或达到预设...