R: NN - Nearest Neighbors Superclass

NN {dbscan} | 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'
    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()`, `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
    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)

[Package dbscan version 1.1-10 Index]