Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Related Algorithms
Find Connected Components in a Nearest-neighbor Graph
Turn an dbscan clustering object into a tidy tibble
dbscan: Density-Based Spatial Clustering of Applications with Noise (D...
Density-based Spatial Clustering of Applications with Noise (DBSCAN)
Coersions to Dendrogram
Framework for the Optimal Extraction of Clusters from Hierarchies
Find the Fixed Radius Nearest Neighbors
Global-Local Outlier Score from Hierarchies
Hierarchical DBSCAN (HDBSCAN)
Plot Convex Hulls of Clusters
Jarvis-Patrick Clustering
Find the k Nearest Neighbors
Calculate and Plot k-Nearest Neighbor Distances
Local Outlier Factor Score
NN --- Nearest Neighbors Superclass
Ordering Points to Identify the Clustering Structure (OPTICS)
Calculate Local Density at Each Data Point
Reachability Distances
Find Shared Nearest Neighbors
Shared Nearest Neighbor Clustering
A fast reimplementation of several density-based algorithms of the DBSCAN family. Includes the clustering algorithms DBSCAN (density-based spatial clustering of applications with noise) and HDBSCAN (hierarchical DBSCAN), the ordering algorithm OPTICS (ordering points to identify the clustering structure), shared nearest neighbor clustering, and the outlier detection algorithms LOF (local outlier factor) and GLOSH (global-local outlier score from hierarchies). The implementations use the kd-tree data structure (from library ANN) for faster k-nearest neighbor search. An R interface to fast kNN and fixed-radius NN search is also provided. Hahsler, Piekenbrock and Doran (2019) <doi:10.18637/jss.v091.i01>.