Through the adjacency matrix, computing networks, clustering coefficient. Clustering coefficient is a complex network, an important parameter.
通过邻接矩阵，计算网络的聚类系数。聚类系数是复杂网络中一个重要参量。
The support vector clustering(SVC)algorithm was introduced to detect linear modulation frequency(LFM) signal and estimate its parameter.
文中引入支持向量聚类（SVC）算法对多分量LFM信号进行检测和参数估计。
Kernel parameter of the SVC algorithm plays an important role in clustering formation, which affects the boundary and shape of cluster.
SVC算法中的核函数参数对聚类的形成起着决定性的作用，并影响着聚类的边界和形状。
Modifying the objective function of FCM and introducing a variable as the parameter to control the tight degree of neighborhood effect present a spatial model to FCM clustering algorithm.
本文改进了传统FCM的目标函数，引入控制邻域作用紧密程度的参数，提出了一种能够更加合理地运用图像的空间信息，改进的模糊c -均值聚类算法。
Modifying the objective function of FCM and introducing a variable as the parameter to control the tight degree of neighborhood effect present a spatial model to FCM clustering algorithm.
本文改进了传统FCM的目标函数，引入控制邻域作用紧密程度的参数，提出了一种能够更加合理地运用图像的空间信息，改进的模糊c -均值聚类算法。
应用推荐