在室内可见光通信中符号间干扰和噪声会严重影响系统性能,K均值(K-means)均衡方法可以抑制光无线信道的影响,但其复杂度较高,且在聚类边界处易出现误判。提出了改进聚类中心点的K-means(Improved Center K-means,IC-Kmeans)算法,通过随...在室内可见光通信中符号间干扰和噪声会严重影响系统性能,K均值(K-means)均衡方法可以抑制光无线信道的影响,但其复杂度较高,且在聚类边界处易出现误判。提出了改进聚类中心点的K-means(Improved Center K-means,IC-Kmeans)算法,通过随机生成足够长的训练序列,然后将训练序列每一簇的均值作为K-means聚类中心,避免了传统K-means反复迭代寻找聚类中心。进一步,提出了基于神经网络的IC-Kmeans(Neural Network Based IC-Kmeans,NNIC-Kmeans)算法,使用反向传播神经网络将接收端二维数据映射至三维空间,以增加不同簇之间混合数据的距离,提高了分类准确性。蒙特卡罗误码率仿真表明,IC-Kmeans均衡和传统K-means算法的误码率性能相当,但可以显著降低复杂度,特别是在信噪比较小时。同时,在室内多径信道模型下,与IC-Kmeans和传统Kmeans均衡相比,NNIC-Kmeans均衡的光正交频分复用系统误码率性能最好。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
针对传统的k-means算法的聚类数目k无法确定、初始聚类中心随机给定、容易受到离群点影响等问题,该算法使用LOF (Local Outlier Factor)离群点检测算法计算数据集中每个数据对象的离群因子,并去除离群因子大于指定阈值的数据对象,使用...针对传统的k-means算法的聚类数目k无法确定、初始聚类中心随机给定、容易受到离群点影响等问题,该算法使用LOF (Local Outlier Factor)离群点检测算法计算数据集中每个数据对象的离群因子,并去除离群因子大于指定阈值的数据对象,使用手肘法来确定符合数据集的最佳k值,根据最大密度和最大距离的思想结合每个点的离群因子来选取初始聚类中心并进行后续聚类中心的迭代,聚类完成后结合三支决策的思想对聚类结果的每个簇内的数据对象进行进一步优化。实验结果表明ODT-kmeans算法能合理选取k值、减少离群点的影响并且可以消除随机选择初始聚类中心的问题,提高了k-means聚类算法的准确率。In view of the problems of the traditional k-means algorithm, such as the number of clusters k cannot be determined, the initial cluster center is randomly given, and it is easily affected by outliers, this algorithm uses the LOF (Local Outlier Factor) outlier detection algorithm to calculate the outlier factor of each data object in the data set and remove the data objects whose outlier factor is greater than the specified threshold. The elbow method is used to determine the best k value that meets the data set. The initial cluster center is selected based on the idea of maximum density and maximum distance combined with the outlier factor of each point and the subsequent cluster center iterations are performed. After clustering is completed, the idea of three-way decision is combined to further optimize the data objects in each cluster of the clustering results. Experimental results show that the ODT-kmeans algorithm can reasonably select the k value, reduce the influence of outliers, and eliminate the problem of randomly selecting the initial cluster center, thereby improving the accuracy of the k-means clustering algorithm.展开更多
雷达信号分选是电子战系统中的关键技术,是战场态势感知的重要环节,新体制雷达技术的快速发展给复杂电磁环境下信号分选带来了严峻挑战。针对传统K-means聚类算法在对雷达全脉冲数据进行信号分选时存在对聚类数K和初始点选择较为敏感的...雷达信号分选是电子战系统中的关键技术,是战场态势感知的重要环节,新体制雷达技术的快速发展给复杂电磁环境下信号分选带来了严峻挑战。针对传统K-means聚类算法在对雷达全脉冲数据进行信号分选时存在对聚类数K和初始点选择较为敏感的问题,提出了一种基于优化K-means的雷达信号分选算法。通过将水波中心扩散(water wave center diffusion,WWCD)优化算法和Canopy算法相结合,实现了Canopy算法距离阈值的优选,并为后续K-means聚类优化了K值的选择,有效降低了K-means算法对初始聚类数选择的敏感性。实验中,主要通过3个UCI公开数据集和3类频率跳变雷达脉冲数据进行聚类分选效果验证,并与常见的DBSCAN、OPTICS、Canopy-K-means等聚类算法进行了聚类效果对比。结果表明,所提方法有较高的聚类分选准确率,且对初始参数的设置不敏感。展开更多
文摘在室内可见光通信中符号间干扰和噪声会严重影响系统性能,K均值(K-means)均衡方法可以抑制光无线信道的影响,但其复杂度较高,且在聚类边界处易出现误判。提出了改进聚类中心点的K-means(Improved Center K-means,IC-Kmeans)算法,通过随机生成足够长的训练序列,然后将训练序列每一簇的均值作为K-means聚类中心,避免了传统K-means反复迭代寻找聚类中心。进一步,提出了基于神经网络的IC-Kmeans(Neural Network Based IC-Kmeans,NNIC-Kmeans)算法,使用反向传播神经网络将接收端二维数据映射至三维空间,以增加不同簇之间混合数据的距离,提高了分类准确性。蒙特卡罗误码率仿真表明,IC-Kmeans均衡和传统K-means算法的误码率性能相当,但可以显著降低复杂度,特别是在信噪比较小时。同时,在室内多径信道模型下,与IC-Kmeans和传统Kmeans均衡相比,NNIC-Kmeans均衡的光正交频分复用系统误码率性能最好。
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.
文摘针对传统的k-means算法的聚类数目k无法确定、初始聚类中心随机给定、容易受到离群点影响等问题,该算法使用LOF (Local Outlier Factor)离群点检测算法计算数据集中每个数据对象的离群因子,并去除离群因子大于指定阈值的数据对象,使用手肘法来确定符合数据集的最佳k值,根据最大密度和最大距离的思想结合每个点的离群因子来选取初始聚类中心并进行后续聚类中心的迭代,聚类完成后结合三支决策的思想对聚类结果的每个簇内的数据对象进行进一步优化。实验结果表明ODT-kmeans算法能合理选取k值、减少离群点的影响并且可以消除随机选择初始聚类中心的问题,提高了k-means聚类算法的准确率。In view of the problems of the traditional k-means algorithm, such as the number of clusters k cannot be determined, the initial cluster center is randomly given, and it is easily affected by outliers, this algorithm uses the LOF (Local Outlier Factor) outlier detection algorithm to calculate the outlier factor of each data object in the data set and remove the data objects whose outlier factor is greater than the specified threshold. The elbow method is used to determine the best k value that meets the data set. The initial cluster center is selected based on the idea of maximum density and maximum distance combined with the outlier factor of each point and the subsequent cluster center iterations are performed. After clustering is completed, the idea of three-way decision is combined to further optimize the data objects in each cluster of the clustering results. Experimental results show that the ODT-kmeans algorithm can reasonably select the k value, reduce the influence of outliers, and eliminate the problem of randomly selecting the initial cluster center, thereby improving the accuracy of the k-means clustering algorithm.
文摘雷达信号分选是电子战系统中的关键技术,是战场态势感知的重要环节,新体制雷达技术的快速发展给复杂电磁环境下信号分选带来了严峻挑战。针对传统K-means聚类算法在对雷达全脉冲数据进行信号分选时存在对聚类数K和初始点选择较为敏感的问题,提出了一种基于优化K-means的雷达信号分选算法。通过将水波中心扩散(water wave center diffusion,WWCD)优化算法和Canopy算法相结合,实现了Canopy算法距离阈值的优选,并为后续K-means聚类优化了K值的选择,有效降低了K-means算法对初始聚类数选择的敏感性。实验中,主要通过3个UCI公开数据集和3类频率跳变雷达脉冲数据进行聚类分选效果验证,并与常见的DBSCAN、OPTICS、Canopy-K-means等聚类算法进行了聚类效果对比。结果表明,所提方法有较高的聚类分选准确率,且对初始参数的设置不敏感。
