A novel multivariate similarity clustering analysis (MSCA) approach was used to estimate a biogeographical division scheme for the global terrestrial fauna and was compared against other widely used clustering algorit...A novel multivariate similarity clustering analysis (MSCA) approach was used to estimate a biogeographical division scheme for the global terrestrial fauna and was compared against other widely used clustering algorithms. The faunal dataset included almost all terrestrial and freshwater fauna, a total of 4631 families, 141,814 genera, and 1,334,834 species. Our findings demonstrated that suitable results were only obtained with the MSCA method, which was associated with distinct hierarchies, reasonable structuring, and furthermore, conformed to biogeographical criteria. A total of seven kingdoms and 20 sub-kingdoms were identified. We discovered that the clustering results for the higher and lower animals did not differ significantly, leading us to consider that the analysis result is convincing as the first zoogeographical division scheme for global all terrestrial animals.展开更多
多变量时间序列(Multivariate Time Series,MTS)具有多变量性和高冗余性,使用聚类分析从海量、高维的MTS数据中挖掘有趣模式具有重要意义。本文从基于实例、基于特征和基于模型的角度,对近年来MTS聚类方法的研究进行归类,为研究者了解...多变量时间序列(Multivariate Time Series,MTS)具有多变量性和高冗余性,使用聚类分析从海量、高维的MTS数据中挖掘有趣模式具有重要意义。本文从基于实例、基于特征和基于模型的角度,对近年来MTS聚类方法的研究进行归类,为研究者了解最新的MTS聚类方法研究动态和发展趋势提供参考。展开更多
文摘A novel multivariate similarity clustering analysis (MSCA) approach was used to estimate a biogeographical division scheme for the global terrestrial fauna and was compared against other widely used clustering algorithms. The faunal dataset included almost all terrestrial and freshwater fauna, a total of 4631 families, 141,814 genera, and 1,334,834 species. Our findings demonstrated that suitable results were only obtained with the MSCA method, which was associated with distinct hierarchies, reasonable structuring, and furthermore, conformed to biogeographical criteria. A total of seven kingdoms and 20 sub-kingdoms were identified. We discovered that the clustering results for the higher and lower animals did not differ significantly, leading us to consider that the analysis result is convincing as the first zoogeographical division scheme for global all terrestrial animals.
文摘多变量时间序列(Multivariate Time Series,MTS)具有多变量性和高冗余性,使用聚类分析从海量、高维的MTS数据中挖掘有趣模式具有重要意义。本文从基于实例、基于特征和基于模型的角度,对近年来MTS聚类方法的研究进行归类,为研究者了解最新的MTS聚类方法研究动态和发展趋势提供参考。