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harthur
448 Stars 83 Forks MIT License 25 Commits 11 Opened issues

Description

[UNMAINTAINED] K-means and hierarchical clustering

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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

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:
[
  [[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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