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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]
];

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

The second argument to cluster is the number of clusters you want (default is Math.sqrt(n/2) where n is the number of vectors). The cluster() method returns an array of 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]]
]

Classification

For classification, instantiate a new Kmeans() object.

var kmeans = new clusterfck.Kmeans();

// Calculate clusters.
var clusters = kmeans.cluster(colors, 3);

// Calculate cluster index for a new data point.
var clusterIndex = kmeans.classify([0, 0, 225]);

Serialization

The toJSON() and fromJSON() methods are available for serialization.

// Serialize centroids to JSON.
var json = kmeans.toJSON();

// Deserialize centroids from JSON.
kmeans = kmeans.fromJSON(json);

// Calculate cluster index from a previously serialized set of centroids.
var clusterIndex = kmeans.classify([0, 0, 225]);

Initializing with Existing Centroids

// Take existing centroids, perhaps from a database?
var centroids = [ [ 35.5, 31.5, 85 ], [ 250, 255, 253 ], [ 227.5, 6.5, 15.5 ] ];

// Initialize constructor with centroids.
var kmeans = new clusterfck.Kmeans(centroids);

// Calculate cluster index.
var clusterIndex = kmeans.classify([0, 0, 225]);

Accessing Centroids and K value

After clustering or loading via fromJSON(), the calculated centers are accessible via the centroids property. Similarly, the K-value can be derived via centroids.length.

// Calculate clusters.
var clusters = kmeans.cluster(colors, 3);

// Access centroids, an array of length 3.
var centroids = kmeans.centroids;

// Access k-value.
var k = centroids.length;

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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K-means and hierarchical clustering

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