Super fast simple k-means and k-means++ clustering for unidimiensional and multidimensional data. Works in node and browser
Super fast simple k-means and k-means++ implementation for unidimiensional and multidimensional data. Works on nodejs and browser.
npm install skmeans
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);
<!doctype html>
<html>
<head>
<script src="skmeans.js"></script>
</head>
<body>
<script>
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);
</script>
</body>
</html>
{
it: 2,
k: 3,
idxs: [ 2, 0, 0, 2, 1, 1, 1, 2, 0, 2, 0, 2, 1, 1, 0 ],
centroids: [ 13, 23, 3 ]
}
Calculates unidimiensional and multidimensional k-means clustering on data. Parameters are:
The function will return an object with the following data:
// 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));
kmeans-js is another JavaScript implementation of the k-means clustering algorithm. It offers similar functionality to skmeans but includes additional features like support for different distance metrics and more advanced initialization methods. It is slightly more complex but provides more flexibility for advanced users.
ml-kmeans is part of the machine learning library 'ml' and provides a robust implementation of the k-means algorithm. It is designed to work seamlessly with other machine learning tools in the 'ml' ecosystem, making it a good choice for more comprehensive machine learning projects. It offers more advanced options and better integration with other machine learning algorithms compared to skmeans.
simple-statistics is a library that provides a wide range of statistical tools, including k-means clustering. While it is not solely focused on k-means, it offers a comprehensive set of statistical functions that can be useful for data analysis. It is a good choice if you need a broader set of statistical tools in addition to k-means clustering.
FAQs
Super fast simple k-means and k-means++ clustering for unidimiensional and multidimensional data. Works in node and browser
The npm package skmeans receives a total of 1,035,572 weekly downloads. As such, skmeans popularity was classified as popular.
We found that skmeans demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 1 open source maintainer collaborating on the project.
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