An algorithm of Gaussian kernel clustering is proposed by analyzing kernel mapping theory.
通过研究核映射机理，提出了用于聚类分析的高斯核聚类算法。
An kernel clustering intrusion detection approach based on outlier detection is presented in this paper.
提出了一种基于孤立点检测的核聚类入侵检测方法。
The modified kernel clustering algorithm is faster than the classical algorithm in convergence and more accurate in clustering.
该聚类方法在性能上比经典聚类算法有所改进，具有更快的收敛速度以及较高的准确度。
Then the simplified method of kernel clustering was used to reduce the computational complexity and improve the robustness of algorithm.
采用核聚类简化的方法降低计算复杂度，提高算法的鲁棒性。
Due to the problems of infrared image segmentation using fuzzy kernel clustering, an improved method for infrared image segmentation was proposed.
针对模糊核聚类对红外图像分割存在的不足，提出了一种改进的模糊核聚类红外图像分割算法。
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。
The experimental results show that the proposed algorithm is superior to the improved kernel clustering algorithm and K-means in good astringency, stability and overall optimal solutions.
实验结果表明，使用该算法的聚类比改进的核聚算法、K均值算法等单一方法具有良好的收敛性、稳定性和更高的全局最优。
The method mines information on overlap between classes, designs the tree structure and overcomes the misclassification of tree-structured SVMs based on the semi-fuzzy kernel clustering algorithm.
该方法基于半模糊核聚类算法挖掘不同类别之间的衔接和离散信息，设计树型支持向量机的树型结构，克服其差错积累问题。
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。
Based on the analysis of the core concepts of the kernel methods, a clustering algorithm based on kernel methods was put forward.
在分析核方法的核心概念基础上，提出了一种基于核方法的聚类算法。
This method USES kernel density estimation model to construct the approximate density function, and takes hill climbing strategy to extract clustering patterns.
该方法采用核密度估计模型来构造近似密度函数，利用爬山策略来提取聚类模式。
This thesis deals with gray image. The kernel of segmentation is pixel clustering. It belongs to optimization problem.
本文处理的对象是灰度图像，分割的核心是对像素进行聚类，属于优化问题。
Kernel function is introduced to subspace clustering to improve the clustering performance.
在子空间聚类引入核函数以提高数据聚类性能。
It studies kernel function method with parameters optimized and its application in pattern clustering.
研究了具有参数优化的核函数法及其在聚类问题中的应用。
Rough set was applied to clustering method in view of soft kernel of support vector clustering(SVC).
支持向量聚类是基于支持向量机和核方法的一种新颖的聚类方法。
Rough set was applied to clustering method in view of soft kernel of support vector clustering(SVC).
支持向量聚类是基于支持向量机和核方法的一种新颖的聚类方法。
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