NN: NN - Nearest Neighbors Superclass

In dbscan: Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Related Algorithms

NN — Nearest Neighbors Superclass

Description

NN is an abstract S3 superclass for the classes of the objects returned by kNN(), frNN() and sNN(). Methods for sorting, plotting and getting an adjacency list are defined.

Usage

adjacencylist(x, ...)

## S3 method for class 'NN'
adjacencylist(x, ...)

## S3 method for class 'NN'
sort(x, decreasing = FALSE, ...)

## S3 method for class 'NN'
plot(x, data, main = NULL, pch = 16, col = NULL, linecol = "gray", ...)

Arguments

x	a NN object
...	further parameters past on to plot().
decreasing	sort in decreasing order?
data	that was used to create x
main	title
pch	plotting character.
col	color used for the data points (nodes).
linecol	color used for edges.

Subclasses

kNN, frNN and sNN

Author(s)

Michael Hahsler

See Also

Other NN functions: comps(), frNN(), kNN(), kNNdist(), sNN()

Examples

data(iris)
x <- iris[, -5]

# finding kNN directly in data (using a kd-tree)
nn <- kNN(x, k=5)
nn

# plot the kNN where NN are shown as line conecting points.
plot(nn, x)

# show the first few elements of the adjacency list
head(adjacencylist(nn))

## Not run: 
# create a graph and find connected components (if igraph is installed)
library("igraph")
g <- graph_from_adj_list(adjacencylist(nn))
comp <- components(g)
plot(x, col = comp$membership)

# detect clusters (communities) with the label propagation algorithm
cl <- membership(cluster_label_prop(g))
plot(x, col = cl)

## End(Not run)

dbscan documentation built on June 29, 2024, 1:07 a.m.