hierarchical-clustering

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Agglomerative Hierarchical clustering based on How to Explain Hierarchical Clustering.

Example

From clusterfck:

``js
var cluster = require('hierarchical-clustering');
var colors = [
[20, 20, 80],
[22, 22, 90],
[250, 255, 253],
[100, 54, 255]
];

// Euclidean distance
function distance(a, b) {
var d = 0;
for (var i = 0; i < a.length; i++) {
d += Math.pow(a[i] - b[i], 2);
}
return Math.sqrt(d);
}

// Single-linkage clustering
function linkage(distances) {
return Math.min.apply(null, distances);
}

var levels = cluster({
input: colors,
distance: distance,
linkage: linkage,
minClusters: 2, // only want two clusters
});

var clusters = levels[levels.length - 1].clusters;
console.log(clusters);
// => [ [ 2 ], [ 3, 1, 0 ] ]
clusters = clusters.map(function (cluster) {
return cluster.map(function (index) {
return colors[index];
});
});
console.log(clusters);
// => [ [ [ 250, 255, 253 ] ],
// => [ [ 100, 54, 255 ], [ 22, 22, 90 ], [ 20, 20, 80 ] ] ]
`

API

`js
var cluster = require('hierarchical-clustering')
`

$3

Options:

- .input (required) - input array
- .distance (required) - distance function
- .linkage (required) - linkage function or string of 'single', 'complete or 'average'
- .minClusters (1) - number of clusters you want to iterate to
- .maxLinkage (Infinity) - maximum linkage until you stop iteration

Function definitions:

`js
function distance(a, b) {
// the smaller, the closer
return
}

function linkage(distances) {
// the smaller, the more similar
return
}
`

The output is an array of levels, each which have the property:

- .linkage - the linkage of this level
- .cluster - the clusters at this level
- .from - index of the last cluster which got merged and deleted
- .to ` - index of the last cluster which got merged and appended

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