A js cluster analysis library. Includes Hierarchical (agglomerative) clustering and K-means clustering. Demo here.
For node.js:
npm install clusterfck
Or grab the browser file
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]]
]
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]
}
}
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");