Package index

Clustering Algorithms

Cluster data with DBSCAN, OPTICS, HDBSCAN, shared nearest neighbor, and Jarvis-Patrick algorithms.

DBSCAN

Density-based spatial clustering of applications with noise.

dbscan() is.corepoint() predict(<dbscan_fast>)

Density-based Spatial Clustering of Applications with Noise (DBSCAN)

OPTICS

Ordering points to identify the clustering structure.

optics() print(<optics>) plot(<optics>) as.reachability(<optics>) as.dendrogram(<optics>) extractDBSCAN() extractXi() predict(<optics>)

Ordering Points to Identify the Clustering Structure (OPTICS)

print(<reachability>) plot(<reachability>) as.reachability()

Reachability Distances

HDBSCAN

Hierarchical DBSCAN, its minimum spanning tree hierarchy, and the extraction of clusters from hierarchies.

hdbscan() print(<hdbscan>) plot(<hdbscan>) coredist() mrdist() predict(<hdbscan>)

Hierarchical DBSCAN (HDBSCAN)

extractFOSC()

Framework for the Optimal Extraction of Clusters from Hierarchies

mst_dist() mst_mrdist()

Minimum Spanning Tree Hierarchies

Shared Nearest Neighbor Clustering

Clustering based on shared nearest neighbor graphs.

sNNclust()

Shared Nearest Neighbor Clustering

jpclust()

Jarvis-Patrick Clustering

Nearest Neighbor Search

Find k-nearest, fixed-radius, and shared nearest neighbors and work with nearest-neighbor graphs.

kNN() sort(<kNN>) adjacencylist(<kNN>) print(<kNN>)

Find the k Nearest Neighbors

frNN() sort(<frNN>) adjacencylist(<frNN>) print(<frNN>)

Find the Fixed Radius Nearest Neighbors

sNN() sort(<sNN>) print(<sNN>)

Find Shared Nearest Neighbors

kNNdist() kNNdistplot()

Calculate and Plot k-Nearest Neighbor Distances

adjacencylist() sort(<NN>) plot(<NN>)

NN — Nearest Neighbors Superclass

comps()

Find Connected Components in a Nearest-neighbor Graph

Outlier Detection

Calculate local and hierarchical outlier scores and estimate local point density.

lof()

Local Outlier Factor Score

glosh()

Global-Local Outlier Score from Hierarchies

pointdensity()

Calculate Local Density at Each Data Point

kNNdist() kNNdistplot()

Calculate and Plot k-Nearest Neighbor Distances

Cluster Validation

Summarize, evaluate, and visualize clusterings, including reachability plots and cluster hierarchies.

ncluster() nnoise()

Number of Clusters, Noise Points, and Observations

dbcv()

Density-Based Clustering Validation Index (DBCV)

hullplot() clplot()

Plot Clusters

as.dendrogram()

Coercions to Dendrogram

Tidyverse Tidiers

Turn clustering objects into tidy tibbles and augment the original data.

tidy() augment() glance()

Turn a dbscan clustering object into a tidy tibble

Data Sets

Example and benchmark data sets for density-based clustering.

moons

Moons Data

DS3

DS3: Spatial data with arbitrary shapes

DBCV_datasets Dataset_1 Dataset_2 Dataset_3 Dataset_4

DBCV Paper Datasets