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https://github.com/solzimer/skmeans
Super fast simple k-means implementation for unidimiensional and multidimensional data.
https://github.com/solzimer/skmeans
browser centroid cluster fast javascript k-means k-means-clustering kmeans kmeans-algorithm kmeans-clustering multidimensional nodejs unidimensional
Last synced: about 1 month ago
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Super fast simple k-means implementation for unidimiensional and multidimensional data.
- Host: GitHub
- URL: https://github.com/solzimer/skmeans
- Owner: solzimer
- Created: 2017-06-05T15:49:35.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2023-01-03T15:16:18.000Z (over 1 year ago)
- Last Synced: 2024-07-05T14:17:24.670Z (2 months ago)
- Topics: browser, centroid, cluster, fast, javascript, k-means, k-means-clustering, kmeans, kmeans-algorithm, kmeans-clustering, multidimensional, nodejs, unidimensional
- Language: JavaScript
- Homepage:
- Size: 503 KB
- Stars: 70
- Watchers: 4
- Forks: 9
- Open Issues: 18
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# skmeans
Super fast simple k-means and [k-means++](https://en.wikipedia.org/wiki/K-means%2B%2B) implementation for unidimiensional and multidimensional data. Works on nodejs and browser.
## Installation
```
npm install skmeans
```
## Usage
### NodeJS
```javascript
const skmeans = require("skmeans");
var data = [1,12,13,4,25,21,22,3,14,5,11,2,23,24,15];
var res = skmeans(data,3);
```
### Browser
```html
var data = [1,12,13,4,25,21,22,3,14,5,11,2,23,24,15];
var res = skmeans(data,3);
console.log(res);
```
## Results
```javascript
{
it: 2,
k: 3,
idxs: [ 2, 0, 0, 2, 1, 1, 1, 2, 0, 2, 0, 2, 1, 1, 0 ],
centroids: [ 13, 23, 3 ]
}
```
## API
### skmeans(data,k,[centroids],[iterations])
Calculates unidimiensional and multidimensional k-means clustering on *data*. Parameters are:
* **data** Unidimiensional or multidimensional array of values to be clustered. for unidimiensional data, takes the form of a simple array *[1,2,3.....,n]*. For multidimensional data, takes a
NxM array *[[1,2],[2,3]....[n,m]]*
* **k** Number of clusters
* **centroids** Optional. Initial centroid values. If not provided, the algorith will try to choose an apropiate ones. Alternative values can be:
* **"kmrand"** Cluster initialization will be random, but with extra checking, so there will no be two equal initial centroids.
* **"kmpp"** The algorythm will use the [k-means++](https://en.wikipedia.org/wiki/K-means%2B%2B) cluster initialization method.
* **iterations** Optional. Maximum number of iterations. If not provided, it will be set to 10000.
* **distance function** Optional. Custom distance function. Takes two points as arguments and returns a scalar number.
The function will return an object with the following data:
* **it** The number of iterations performed until the algorithm has converged
* **k** The cluster size
* **centroids** The value for each centroid of the cluster
* **idxs** The index to the centroid corresponding to each value of the data array
* **test** Function to test new point membership
## Examples
```javascript
// k-means with 3 clusters. Random initialization
var res = skmeans(data,3);
// k-means with 3 clusters. Initial centroids provided
var res = skmeans(data,3,[1,5,9]);
// k-means with 3 clusters. k-means++ cluster initialization
var res = skmeans(data,3,"kmpp");
// k-means with 3 clusters. Random initialization. 10 max iterations
var res = skmeans(data,3,null,10);
// k-means with 3 clusters. Custom distance function
var res = skmeans(data,3,null,null,(x1,x2)=>Math.abs(x1-x2));
// Test new point
var res = skmeans(data,3,null,10);
res.test(6);
// Test new point with custom distance
var res = skmeans(data,3,null,10);
res.test(6,(x1,x2)=>Math.abs(x1-x2));
```