# jhcluster

> Hierarchical clustering algorithm

Latest version **1.0.2** (published 2020-02-25) · MIT license · 0 weekly downloads

## Install

```sh
npm install jhcluster
pnpm add jhcluster
yarn add jhcluster
bun add jhcluster
```

## Health

**Score 15/100 (F)** — status: abandoned.

Positive: no vulnerabilities.

Warnings: low downloads; no types; no esm support.

Negative: abandoned; low maintenance score.

## Facts

| | |
|---|---|
| Version | 1.0.2 |
| Published | 2020-02-25 |
| First published | 2020-02-25 |
| Weekly downloads | 0 |
| License | MIT |
| TypeScript types | none |
| Module format | CommonJS |
| Dependencies | 0 |
| Unpacked size | 67.1 KB |
| Known vulnerabilities | 0 |
| Install scripts | no |
| GitHub stars | 9 |
| Author | Corneliu S. |
| Maintainers | upphiminn |
| Keywords | hierarchical clustering, clustering, algorithms, computation, gps clustering, gps |

## Links

- npm: https://www.npmjs.com/package/jhcluster
- Repository: https://github.com/upphiminn/jHC
- Homepage: https://github.com/upphiminn/jHC#readme
- Issues: https://github.com/upphiminn/jHC/issues
- npm.io page: https://npm.io/package/jhcluster

## Recent versions

- 1.0.2 (latest) — 2020-02-25
- 1.0.1 — 2020-02-25
- 1.0.0 — 2020-02-25

## README

# jHC

## Description

Hierarchical clustering **(agglomerative)** is a clustering algorithm that builds a cluster hierarchy from some given points **[1]**.

**[1]** http://en.wikipedia.org/wiki/Hierarchical_clustering

## Additional Features

Besides n-dimensional data, the algorithm also works on data points given by **_GPS coordinates_**.

## Usage

1. Import the script.

```html
<script type="text/javascript" src="jHC.js"></script>
```

2. Sample Data Format

### Basic Data

```javascript
let point_data = [
	[0.1, 5],
	[2, 4],
	[0, 7]
];
```

### GPS Data

The **latitude** is the first number in the pair followed by the **longitude**.

```javascript
let gps_point_data = [
	[55.7858667, 12.5233995],
	[45.4238667, 12.5233995],
	[25.3438667, 11.6533995]
];
```

3. Run the algorithm. To run the algorithm you need to provide the data along with the **linkage** and **distance** parameters. For the traditional **hierarchical clustering** the steps are the following:

```javascript
let hc = jHC()
	.linkage('AVERAGE')
	.distance('EUCLIDEAN')
	.data(point_data);
```

The distance functions available are: **'EUCLIDEAN', 'HAVERSINE'** (for GPS data), **'MANHATTAN'**. The linkages implemented are **'AVERAGE', 'COMPLETE'** and **'SINGLE'**. Additionally you can provide your own distance function, which must accept at least two parameters (the two points), and then pass it to the _distance_ method as its parameter. The next step is to simply run the clustering algorithm.

```javascript
// This will run the aglorithm and return the resulting hierarchy tree.
let hierarchy_tree = hc();
```

### Result Format

The end root node is wrapped in an array and returned. The resulting clustering hierarchy has the following format:

```javascript
[
	{
		children: [
			// the two children of the root hierarchy node.
			{
				children: [
					{
						//leaf node
						coordinates: [9, 5],
						name: 1,
						size: 1
					},
					{
						//leaf node
						coordinates: [7, 9],
						name: 2,
						size: 1
					}
				],
				coordinates: [11, 12],
				name: 3,
				size: 2
			},
			{
				//leaf node
				coordinates: [9, 9],
				name: 0,
				size: 1
			}
		],
		coordinates: [13, 14], // centroid coordinates for the cluster
		name: 4, // id of cluster
		size: 3 // number of points contained
	}
];
```

Additionally you can get the leaf nodes with the _leafNodes_ function.

### Example

See working example in the **example/example.html** file. Use the console to inspect the input and output of the algorithm.

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