# NN: NN - Nearest Neighbors Superclass In dbscan: Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Related Algorithms

 NN R Documentation

## 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'

## 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

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

## Not run:
# create a graph and find connected components (if igraph is installed)
library("igraph")
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 Oct. 29, 2022, 1:13 a.m.