Clustering high dimensional data is challenging as data dimensionality increases the distance between data points,resulting in sparse regions that degrade clustering performance.Subspace clustering is a common approac...Clustering high dimensional data is challenging as data dimensionality increases the distance between data points,resulting in sparse regions that degrade clustering performance.Subspace clustering is a common approach for processing high-dimensional data by finding relevant features for each cluster in the data space.Subspace clustering methods extend traditional clustering to account for the constraints imposed by data streams.Data streams are not only high-dimensional,but also unbounded and evolving.This necessitates the development of subspace clustering algorithms that can handle high dimensionality and adapt to the unique characteristics of data streams.Although many articles have contributed to the literature review on data stream clustering,there is currently no specific review on subspace clustering algorithms in high-dimensional data streams.Therefore,this article aims to systematically review the existing literature on subspace clustering of data streams in high-dimensional streaming environments.The review follows a systematic methodological approach and includes 18 articles for the final analysis.The analysis focused on two research questions related to the general clustering process and dealing with the unbounded and evolving characteristics of data streams.The main findings relate to six elements:clustering process,cluster search,subspace search,synopsis structure,cluster maintenance,and evaluation measures.Most algorithms use a two-phase clustering approach consisting of an initialization stage,a refinement stage,a cluster maintenance stage,and a final clustering stage.The density-based top-down subspace clustering approach is more widely used than the others because it is able to distinguish true clusters and outliers using projected microclusters.Most algorithms implicitly adapt to the evolving nature of the data stream by using a time fading function that is sensitive to outliers.Future work can focus on the clustering framework,parameter optimization,subspace search techniques,memory-efficient synopsis structures,explicit cluster change detection,and intrinsic performance metrics.This article can serve as a guide for researchers interested in high-dimensional subspace clustering methods for data streams.展开更多
Stream morphology is an important indicator for revealing the geomorphological features and evolution of the Yangtze River.Existing studies on the morphology of the Yangtze River focus on planar features.However,the v...Stream morphology is an important indicator for revealing the geomorphological features and evolution of the Yangtze River.Existing studies on the morphology of the Yangtze River focus on planar features.However,the vertical features are also important.Vertical features mainly control the flow ability and erosion intensity.Furthermore,traditional studies often focus on a few stream profiles in the Yangtze River.However,stream profiles are linked together by runoff nodes,thus affecting the geomorphological evolution of the Yangtze River naturally.In this study,a clustering method of stream profiles in the Yangtze River is proposed by plotting all profiles together.Then,a stream evolution index is used to investigate the geomorphological features of the stream profile clusters to reveal the evolution of the Yangtze River.Based on the stream profile clusters,the erosion base of the Yangtze River generally changes from steep to gentle from the upper reaches to the lower reaches,and the evolution degree of the stream changes from low to high.The asymmetric distribution of knickpoints in the Hanshui River Basin supports the view that the boundary of the eastward growth of the Tibetan Plateau has reached the vicinity of the Daba Mountains.展开更多
文摘Clustering high dimensional data is challenging as data dimensionality increases the distance between data points,resulting in sparse regions that degrade clustering performance.Subspace clustering is a common approach for processing high-dimensional data by finding relevant features for each cluster in the data space.Subspace clustering methods extend traditional clustering to account for the constraints imposed by data streams.Data streams are not only high-dimensional,but also unbounded and evolving.This necessitates the development of subspace clustering algorithms that can handle high dimensionality and adapt to the unique characteristics of data streams.Although many articles have contributed to the literature review on data stream clustering,there is currently no specific review on subspace clustering algorithms in high-dimensional data streams.Therefore,this article aims to systematically review the existing literature on subspace clustering of data streams in high-dimensional streaming environments.The review follows a systematic methodological approach and includes 18 articles for the final analysis.The analysis focused on two research questions related to the general clustering process and dealing with the unbounded and evolving characteristics of data streams.The main findings relate to six elements:clustering process,cluster search,subspace search,synopsis structure,cluster maintenance,and evaluation measures.Most algorithms use a two-phase clustering approach consisting of an initialization stage,a refinement stage,a cluster maintenance stage,and a final clustering stage.The density-based top-down subspace clustering approach is more widely used than the others because it is able to distinguish true clusters and outliers using projected microclusters.Most algorithms implicitly adapt to the evolving nature of the data stream by using a time fading function that is sensitive to outliers.Future work can focus on the clustering framework,parameter optimization,subspace search techniques,memory-efficient synopsis structures,explicit cluster change detection,and intrinsic performance metrics.This article can serve as a guide for researchers interested in high-dimensional subspace clustering methods for data streams.
基金National Natural Science Foundation of China,No.41930102,No.41971333。
文摘Stream morphology is an important indicator for revealing the geomorphological features and evolution of the Yangtze River.Existing studies on the morphology of the Yangtze River focus on planar features.However,the vertical features are also important.Vertical features mainly control the flow ability and erosion intensity.Furthermore,traditional studies often focus on a few stream profiles in the Yangtze River.However,stream profiles are linked together by runoff nodes,thus affecting the geomorphological evolution of the Yangtze River naturally.In this study,a clustering method of stream profiles in the Yangtze River is proposed by plotting all profiles together.Then,a stream evolution index is used to investigate the geomorphological features of the stream profile clusters to reveal the evolution of the Yangtze River.Based on the stream profile clusters,the erosion base of the Yangtze River generally changes from steep to gentle from the upper reaches to the lower reaches,and the evolution degree of the stream changes from low to high.The asymmetric distribution of knickpoints in the Hanshui River Basin supports the view that the boundary of the eastward growth of the Tibetan Plateau has reached the vicinity of the Daba Mountains.
