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K-means and hierarchical clustering

branch: master
README.md

Clusterfck

A js cluster analysis library. Includes Hierarchical (agglomerative) clustering and K-means clustering. Demo here.

Install

For node.js:

npm install clusterfck

Or grab the browser file

K-means

var clusterfck = require("clusterfck");

var colors = [
   [20, 20, 80],
   [22, 22, 90],
   [250, 255, 253],
   [0, 30, 70],
   [200, 0, 23],
   [100, 54, 100],
   [255, 13, 8]
];

var clusters = clusterfck.kmeans(colors, 3);

The second argument to kmeans is the number of clusters you want (default is Math.sqrt(n/2) where n is the number of vectors). It returns an array of the clusters, for this example:

[
  [[200,0,23], [255,13,8]],
  [[20,20,80], [22,22,90], [0,30,70], [100,54,100]],
  [[250,255,253]]
]

Hierarchical

var clusterfck = require("clusterfck");

var colors = [
   [20, 20, 80],
   [22, 22, 90],
   [250, 255, 253],
   [100, 54, 255]
];

var clusters = clusterfck.hcluster(colors);

hcluster returns an object that represents the hierarchy of the clusters with left and right subtrees. The leaf clusters have a value property which is the vector from the data set.

{
   "left": {
      "left": {
         "left": {
            "value": [22, 22, 90]
         },
         "right": {
            "value": [20, 20, 80]
         },
      },
      "right": {
         "value": [100, 54, 255]
      },
   },
   "right": {
      "value": [250, 255, 253]
   }
}

Distance metric and linkage

Specify the distance metric, one of "euclidean" (default), "manhattan", and "max". The linkage criterion is the third argument, one of "average" (default), "single", and "complete".

var tree = clusterfck.hcluster(colors, "euclidean", "single");
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