Clustering by measuring local direction centrality for data with heterogeneous density and weak connectivity

HIGHLIGHTS

• who: Dehua Peng from the (UNIVERSITY) have published the paper: Clustering by measuring local direction centrality for data with heterogeneous density and weak connectivity, in the Journal: (JOURNAL) of 29/Oct/2020
• what: The authors propose a boundary-seeking Clustering algorithm using the local Direction Centrality (CDC). The authors demonstrate the validity of CDC by detecting complex structured clusters in challenging synthetic datasets, identifying cell types from single-cell_RNA_sequencing (scRNA-seq) and mass cytometry (CyTOF) data, recognizing speakers on voice corpuses, and testifying on various types of real-world benchmarks. In this experiment, the authors . . .
  

   

  

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