https://pypi.org/project/cdc-cluster/ Recent updates to the Python Package Index for cdc-cluster en https://pypi.org/project/cdc-cluster/0.2.3/ 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. pengdh@whu.edu.cn Sat, 14 Mar 2026 16:30:17 GMT https://pypi.org/project/cdc-cluster/0.2.1/ 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. pengdh@whu.edu.cn Thu, 05 Feb 2026 08:34:58 GMT https://pypi.org/project/cdc-cluster/0.2.0/ 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. pengdh@whu.edu.cn Thu, 05 Feb 2026 08:31:23 GMT https://pypi.org/project/cdc-cluster/0.1.1/ 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. pengdh@whu.edu.cn Sat, 13 Sep 2025 05:18:26 GMT https://pypi.org/project/cdc-cluster/0.1.0/ 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. pengdh@whu.edu.cn Sat, 13 Sep 2025 05:01:13 GMT