Michael K.Ng等人提出了新K-Modes聚类算法,它采用基于相对频率的启发式相异度度量方法,有效地提高了聚类精度,但不足的是在计算各类的属性分类值频率时假定类中样本对聚类的贡献相同。为了考虑类中样本对类中心的不同影响,提出一种粗糙...Michael K.Ng等人提出了新K-Modes聚类算法,它采用基于相对频率的启发式相异度度量方法,有效地提高了聚类精度,但不足的是在计算各类的属性分类值频率时假定类中样本对聚类的贡献相同。为了考虑类中样本对类中心的不同影响,提出一种粗糙K-Modes算法,通过粗糙集的上、下近似度量数据样本在类内的重要性程度,不仅可以获得比新K-Modes算法更好的聚类效果,而且可以在保证聚类效果的基础上降低白亮等人提出的基于粗糙集改进的K-Modes算法的计算复杂度。对几个UCI的数据集的测试实验结果显示出新算法的优良性能。展开更多
本文提出了一种基于密度聚类的三支K-Means算法。针对传统的K-Means算法在选取初始聚类中心时往往依赖于随机选择和无法处理不确定性数据对象的问题,本文采用基于密度聚类算法优化初始聚类中心的选择,并优化了截断距离的选取,最后使用...本文提出了一种基于密度聚类的三支K-Means算法。针对传统的K-Means算法在选取初始聚类中心时往往依赖于随机选择和无法处理不确定性数据对象的问题,本文采用基于密度聚类算法优化初始聚类中心的选择,并优化了截断距离的选取,最后使用三支决策的方法对聚类结果进行处理。实验结果表明,与传统的K-Means算法相比,改进的K-Means算法在聚类中表现出更高的聚类精度和稳定性。This paper proposes a three-branch K-Means algorithm based on density clustering. In view of the problem that the traditional K-Means algorithm often relies on random selection and cannot handle uncertain data objects when selecting initial clustering centers, this paper uses a density-based clustering algorithm to optimize the selection of initial clustering centers, and optimizes the selection of truncation distance. Finally, a three-branch decision method is used to process the clustering results. The experimental results show that the improved K-Means algorithm exhibits higher clustering accuracy and stability in clustering compared to the traditional K-Means algorithm.展开更多
文摘本文提出了一种基于密度聚类的三支K-Means算法。针对传统的K-Means算法在选取初始聚类中心时往往依赖于随机选择和无法处理不确定性数据对象的问题,本文采用基于密度聚类算法优化初始聚类中心的选择,并优化了截断距离的选取,最后使用三支决策的方法对聚类结果进行处理。实验结果表明,与传统的K-Means算法相比,改进的K-Means算法在聚类中表现出更高的聚类精度和稳定性。This paper proposes a three-branch K-Means algorithm based on density clustering. In view of the problem that the traditional K-Means algorithm often relies on random selection and cannot handle uncertain data objects when selecting initial clustering centers, this paper uses a density-based clustering algorithm to optimize the selection of initial clustering centers, and optimizes the selection of truncation distance. Finally, a three-branch decision method is used to process the clustering results. The experimental results show that the improved K-Means algorithm exhibits higher clustering accuracy and stability in clustering compared to the traditional K-Means algorithm.
