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http://harthur.github.io/clusterfck/

K-means and hierarchical clustering
http://harthur.github.io/clusterfck/

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K-means and hierarchical clustering

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# Clusterfck

A js [cluster analysis](http://en.wikipedia.org/wiki/Cluster_analysis) library. Includes [Hierarchical (agglomerative) clustering](http://en.wikipedia.org/wiki/Hierarchical_clustering) and [K-means clustering](http://en.wikipedia.org/wiki/K-means_clustering). [Demo here](http://harthur.github.com/clusterfck/demos/colors/).



# Install



For node.js:



```bash

npm install clusterfck

```

Or grab the [browser file](http://harthur.github.com/clusterfck/demos/colors/clusterfck.js)



# K-means



```javascript

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 `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 clusters, for this example:



```javascript

[

  [[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.



```javascript

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.



```javascript

// 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



```javascript

// 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.



```javascript

// 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



```javascript

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.



```javascript

{

   "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"`.



```javascript

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

```