基于持续同调的高维数据聚类算法OA北大核心CSTPCD

A new clustering algorithm called persistent homology based clustering(PHBC)was proposed for high-dimensional data.The data was processed through the perspective of topology,and the topological features of different types of samples were calculated by using the simple complex forms.The topological features were recorded as persistent homology information,and the persistent homology information was converted into vector form,which was used as the input of the clustering algorithm.In this way,the clustering algorithm can process high-dimensional data after preprocessing.Experimental results show that the PHBC algorithm can handle various complex high-dimensional data.Compared with several typical clustering algorithms,PHBC can improve the clustering performance considerably as evaluated by a variety of clustering metrics.Also,the standard deviation of these metrics is smaller,indicating a more stable output of PHBC.