Addressing the issue that flight plans between Chinese city pairs typically rely on a single route,lacking alternative paths and posing challenges in responding to emergencies,this study employs the“quantile-inflecti...Addressing the issue that flight plans between Chinese city pairs typically rely on a single route,lacking alternative paths and posing challenges in responding to emergencies,this study employs the“quantile-inflection point method”to analyze specific deviation trajectories,determine deviation thresholds,and identify commonly used deviation paths.By combining multiple similarity metrics,including Euclidean distance,Hausdorff distance,and sector edit distance,with the density-based spatial clustering of applications with noise(DBSCAN)algorithm,the study clusters deviation trajectories to construct a multi-option trajectory set for city pairs.A case study of 23578 flight trajectories between the Guangzhou airport cluster and the Shanghai airport cluster demonstrates the effectiveness of the proposed framework.Experimental results show that sector edit distance achieves superior clustering performance compared to Euclidean and Hausdorff distances,with higher silhouette coefficients and lower Davies⁃Bouldin indices,ensuring better intra-cluster compactness and inter-cluster separation.Based on clustering results,19 representative trajectory options are identified,covering both nominal and deviation paths,which significantly enhance route diversity and reflect actual flight practices.This provides a practical basis for optimizing flight paths and scheduling,enhancing the flexibility of route selection for flights between city pairs.展开更多
Intuitionistic fuzzy set(IFS)is a set of 2-tuple arguments,each of which is characterized by a membership degree and a nonmembership degree.The generalized form of IFS is interval-valued intuitionistic fuzzy set(IVIFS...Intuitionistic fuzzy set(IFS)is a set of 2-tuple arguments,each of which is characterized by a membership degree and a nonmembership degree.The generalized form of IFS is interval-valued intuitionistic fuzzy set(IVIFS),whose components are intervals rather than exact numbers.IFSs and IVIFSs have been found to be very useful to describe vagueness and uncertainty.However,it seems that little attention has been focused on the clustering analysis of IFSs and IVIFSs.An intuitionistic fuzzy hierarchical algorithm is introduced for clustering IFSs,which is based on the traditional hierarchical clustering procedure,the intuitionistic fuzzy aggregation operator,and the basic distance measures between IFSs:the Hamming distance,normalized Hamming,weighted Hamming,the Euclidean distance,the normalized Euclidean distance,and the weighted Euclidean distance.Subsequently,the algorithm is extended for clustering IVIFSs.Finally the algorithm and its extended form are applied to the classifications of building materials and enterprises respectively.展开更多
提出基于知识度量的模糊粗糙c-均值聚类(fuzzy rough c-means based on the knowledge measure,KFRCM)算法。传统聚类算法在处理具有模糊边界的数据时存在一定的局限性,表现为对初始聚类中心较为敏感且在高维空间中效率较低。为解决上...提出基于知识度量的模糊粗糙c-均值聚类(fuzzy rough c-means based on the knowledge measure,KFRCM)算法。传统聚类算法在处理具有模糊边界的数据时存在一定的局限性,表现为对初始聚类中心较为敏感且在高维空间中效率较低。为解决上述问题,引入特征加权的知识度量,结合模糊隶属度函数与粗糙集近似算子,采用高斯核相似度以增强边界特性。实验采用14个数据集,实验结果表明,KFRCM算法的聚类准确性、稳定性和计算效率均优于6种主流聚类算法。该研究首次将知识度量与模糊粗糙聚类相结合,为开发更为可靠和适应性更强的聚类算法提供了新的思路和算法。展开更多
提出了在输入-输出积空间中利用监督模糊聚类技术快速建立粗糙数据模型(rough data model,简称RDM)的一种方法.该方法将RDM模型的分类质量性能指标与具有良好特性的Gustafson-Kessel(G-K)聚类算法结合在一起,并通过引入数据对模糊类的...提出了在输入-输出积空间中利用监督模糊聚类技术快速建立粗糙数据模型(rough data model,简称RDM)的一种方法.该方法将RDM模型的分类质量性能指标与具有良好特性的Gustafson-Kessel(G-K)聚类算法结合在一起,并通过引入数据对模糊类的推定隶属度的概念,给出了将模糊聚类模型转化为粗糙数据模型的方法,从而设计出一种通过迭代计算使目标函数最小的两个必要条件方程来获取RDM模型的有效算法,将Kowalczyk方法的多维搜索过程变为以聚类数目为参数的一维搜索,极大地减少了寻优时间.与传统的粗糙集理论和Kowalczyk方法相比,提出的方法具有更好的数据概括能力和噪声数据处理能力.最后,通过不同的数据集实验测试,结果表明了该方法的有效性.展开更多
基金supported in part by Boeing Company and Nanjing University of Aeronautics and Astronautics(NUAA)through the Research on Decision Support Technology of Air Traffic Operation Management in Convective Weather under Project 2022-GT-129in part by the Postgraduate Research and Practice Innovation Program of NUAA(No.xcxjh20240709)。
文摘Addressing the issue that flight plans between Chinese city pairs typically rely on a single route,lacking alternative paths and posing challenges in responding to emergencies,this study employs the“quantile-inflection point method”to analyze specific deviation trajectories,determine deviation thresholds,and identify commonly used deviation paths.By combining multiple similarity metrics,including Euclidean distance,Hausdorff distance,and sector edit distance,with the density-based spatial clustering of applications with noise(DBSCAN)algorithm,the study clusters deviation trajectories to construct a multi-option trajectory set for city pairs.A case study of 23578 flight trajectories between the Guangzhou airport cluster and the Shanghai airport cluster demonstrates the effectiveness of the proposed framework.Experimental results show that sector edit distance achieves superior clustering performance compared to Euclidean and Hausdorff distances,with higher silhouette coefficients and lower Davies⁃Bouldin indices,ensuring better intra-cluster compactness and inter-cluster separation.Based on clustering results,19 representative trajectory options are identified,covering both nominal and deviation paths,which significantly enhance route diversity and reflect actual flight practices.This provides a practical basis for optimizing flight paths and scheduling,enhancing the flexibility of route selection for flights between city pairs.
基金supported by the National Natural Science Foundation of China(70571087)the National Science Fund for Distinguished Young Scholars of China(70625005)
文摘Intuitionistic fuzzy set(IFS)is a set of 2-tuple arguments,each of which is characterized by a membership degree and a nonmembership degree.The generalized form of IFS is interval-valued intuitionistic fuzzy set(IVIFS),whose components are intervals rather than exact numbers.IFSs and IVIFSs have been found to be very useful to describe vagueness and uncertainty.However,it seems that little attention has been focused on the clustering analysis of IFSs and IVIFSs.An intuitionistic fuzzy hierarchical algorithm is introduced for clustering IFSs,which is based on the traditional hierarchical clustering procedure,the intuitionistic fuzzy aggregation operator,and the basic distance measures between IFSs:the Hamming distance,normalized Hamming,weighted Hamming,the Euclidean distance,the normalized Euclidean distance,and the weighted Euclidean distance.Subsequently,the algorithm is extended for clustering IVIFSs.Finally the algorithm and its extended form are applied to the classifications of building materials and enterprises respectively.
文摘提出基于知识度量的模糊粗糙c-均值聚类(fuzzy rough c-means based on the knowledge measure,KFRCM)算法。传统聚类算法在处理具有模糊边界的数据时存在一定的局限性,表现为对初始聚类中心较为敏感且在高维空间中效率较低。为解决上述问题,引入特征加权的知识度量,结合模糊隶属度函数与粗糙集近似算子,采用高斯核相似度以增强边界特性。实验采用14个数据集,实验结果表明,KFRCM算法的聚类准确性、稳定性和计算效率均优于6种主流聚类算法。该研究首次将知识度量与模糊粗糙聚类相结合,为开发更为可靠和适应性更强的聚类算法提供了新的思路和算法。
文摘提出了在输入-输出积空间中利用监督模糊聚类技术快速建立粗糙数据模型(rough data model,简称RDM)的一种方法.该方法将RDM模型的分类质量性能指标与具有良好特性的Gustafson-Kessel(G-K)聚类算法结合在一起,并通过引入数据对模糊类的推定隶属度的概念,给出了将模糊聚类模型转化为粗糙数据模型的方法,从而设计出一种通过迭代计算使目标函数最小的两个必要条件方程来获取RDM模型的有效算法,将Kowalczyk方法的多维搜索过程变为以聚类数目为参数的一维搜索,极大地减少了寻优时间.与传统的粗糙集理论和Kowalczyk方法相比,提出的方法具有更好的数据概括能力和噪声数据处理能力.最后,通过不同的数据集实验测试,结果表明了该方法的有效性.
