Node.js asynchronous implementation of the clustering algorithm k-means
$ npm install node-kmeans
// Data source: LinkedIn
var data = [
{'company': 'Microsoft' , 'size': 91259, 'revenue': 60420},
{'company': 'IBM' , 'size': 400000, 'revenue': 98787},
{'company': 'Skype' , 'size': 700, 'revenue': 716},
{'company': 'SAP' , 'size': 48000, 'revenue': 11567},
{'company': 'Yahoo!' , 'size': 14000 , 'revenue': 6426 },
{'company': 'eBay' , 'size': 15000, 'revenue': 8700},
];
// Create the data 2D-array (vectors) describing the data
var vectors = new Array();
for (var i = 0 ; i < data.length ; i++)
vectors[i] = [ data[i]['size'] , data[i]['revenue']];
var kmeans = require('node-kmeans');
kmeans.clusterize(vectors, {k: 4}, function(err,res) {
if (err) console.error(err);
else console.log('%o',res);
});
- 'vectors' is a nXm array (n [lines] : number of points, m [columns] : number of dimensions)
- options object:
- k : number of clusters
- distance : [optional] distance function
An array of objects (one for each cluster) with the following properties:
- centroid : array of X elements (X = number of dimensions)
- cluster : array of X elements containing the vectors of the input data
- clusterInd : array of X integers which are the indexes of the input data
- Technique to avoid local optima (mutation, ...)
Philmod <philippe.modard@gmail.com>