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。
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。
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。
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。
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。
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。
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。
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