node-red-contrib-tak-registration/node_modules/density-clustering/README.md

A Node-RED node to register to TAK and to help wrap files as datapackages to send to TAK

# Density Based Clustering for JavaScript

Package contains popular methods for cluster analysis in data mining:
- DBSCAN
- OPTICS
- K-MEANS

# Overview

### DBSCAN

Density-based spatial clustering of applications with noise (DBSCAN) is one of the most popular algorithm for clustering data.

http://en.wikipedia.org/wiki/DBSCAN

### OPTICS

Ordering points to identify the clustering structure (OPTICS) is an algorithm for clustering data similar to DBSCAN.
The main difference between OPTICS and DBSCAN is that it can handle data of varying densities.

http://en.wikipedia.org/wiki/OPTICS_algorithm

**Important**

Clustering returned by OPTICS is nearly indistinguishable from a clustering created by DBSCAN.
To extract different density-based clustering as well as hierarchical structure you need to analyse **reachability plot** generated by OPTICS.

For more information visit http://en.wikipedia.org/wiki/OPTICS_algorithm#Extracting_the_clusters

### K-MEANS

K-means clustering is one of the most popular method of vector quantization, originally from signal processing.
Although this method is **not density-based**, it's included in the library for completeness.

http://en.wikipedia.org/wiki/K-means_clustering

## Installation

Node:
```bash
npm install density-clustering
```

Browser:
```bash
bower install density-clustering
# build
npm install
gulp
```

## Examples

### DBSCAN
```js
var dataset = [
    [1,1],[0,1],[1,0],
    [10,10],[10,13],[13,13],
    [54,54],[55,55],[89,89],[57,55]
];

var clustering = require('density-clustering');
var dbscan = new clustering.DBSCAN();
// parameters: 5 - neighborhood radius, 2 - number of points in neighborhood to form a cluster
var clusters = dbscan.run(dataset, 5, 2);
console.log(clusters, dbscan.noise);

/*
RESULT:
[
    [0,1,2],
    [3,4,5],
    [6,7,9],
    [8]
]

NOISE: [ 8 ]
*/
```

### OPTICS

```js
// REGULAR DENSITY
var dataset = [
  [1,1],[0,1],[1,0],
  [10,10],[10,11],[11,10],
  [50,50],[51,50],[50,51],
  [100,100]
];

var clustering = require('density-clustering');
var optics = new clustering.OPTICS();
// parameters: 2 - neighborhood radius, 2 - number of points in neighborhood to form a cluster
var clusters = optics.run(dataset, 2, 2);
var plot = optics.getReachabilityPlot();
console.log(clusters, plot);

/*
RESULT:
[
  [0,1,2],
  [3,4,5],
  [6,7,8],
  [9]
]
*/
```

```js
// VARYING DENSITY
var dataset = [
  [0,0],[6,0],[-1,0],[0,1],[0,-1],
  [45,45],[45.1,45.2],[45.1,45.3],[45.8,45.5],[45.2,45.3],
  [50,50],[56,50],[50,52],[50,55],[50,51]
];

var clustering = require('density-clustering');
var optics = new clustering.OPTICS();
// parameters: 6 - neighborhood radius, 2 - number of points in neighborhood to form a cluster
var clusters = optics.run(dataset, 6, 2);
var plot = optics.getReachabilityPlot();
console.log(clusters, plot);

/*
RESULT:
[
  [0, 2, 3, 4],
  [1],
  [5, 6, 7, 9, 8],
  [10, 14, 12, 13],
  [11]
]
*/
```

### K-MEANS
```js
var dataset = [
  [1,1],[0,1],[1,0],
  [10,10],[10,13],[13,13],
  [54,54],[55,55],[89,89],[57,55]
];

var clustering = require('density-clustering');
var kmeans = new clustering.KMEANS();
// parameters: 3 - number of clusters
var clusters = kmeans.run(dataset, 3);
console.log(clusters);

/*
RESULT:
[
  [0,1,2,3,4,5],
  [6,7,9],
  [8]
]
*/
```

## Testing

Open folder and run:
```bash
mocha -R spec
```

## License

Software is licensed under MIT license.
For more information check LICENSE file.