ZPGuiGroupWhu/ClusteringDirectionCentrality
A novel Clustering algorithm by measuring Direction Centrality (CDC) locally. It adopts a density-independent metric based on the distribution of K-nearest neighbors (KNNs) to distinguish between internal and boundary points. The boundary points generate enclosed cages to bind the connections of internal points.
This project helps scientists and researchers analyze complex biological or other data to identify natural groupings within it. You provide your experimental data (like single-cell RNA sequencing or CyTOF data, or even speaker recognition features) and it produces distinct clusters or groups without needing extensive parameter tuning, especially useful when your data has uneven densities or weak connections between groups. This is for biologists, data scientists, or anyone working with complex, high-dimensional datasets that need robust clustering.
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Use this if you need to reliably find distinct clusters in your data, especially if it has varying densities or weak connections between potential groups, and you want to reduce the effort of fine-tuning clustering parameters.
Not ideal if your data is simple, low-dimensional, or has clearly defined, uniform clusters that can be easily identified by standard clustering methods.
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MATLAB
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Last pushed
Sep 13, 2025
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