agglo

Agglo

Fast hierarchical agglomerative clustering in Javascript

Install

npm install agglo

Usage

var levels = agglo(inputs, [options]);

inputs

An array of numbers to measure the distance between.

agglo([0, 1, 2]);

To measure multiple dimensions, use multiple arrays of numbers.

agglo([
  [1, 10, 50.32],
  [9, 3, 18.0]
  [0, 1.5, 9.7]
]);

If you define your own distance function, your inputs can be more abstract.

agglo(db.get('users'), {
  distance: measureUserDistance
});

options

• maxLinkage

Limits clustering to a maximum linkage (distance).

Default: Infinity

Note: This will likely change the number of returned levels

• linkage

Specifies the linkage function to use (default: "average")

• "average"

  Merge clusters based on the average distance between items in each cluster.

• "complete"

  Merge clusters based on the largest distance between items in each cluster.

• "single"

  Merge clusters based on the smallest distance between items in each cluster.

• function (source, target)

  A custom linkage function that returns the distance between the source cluster and the target cluster.

  The source and target look objects like this:

   {
      index: 5,      // the value's index in the original input
      count: 2,      // the number of values in this cluster
      links: [],     // an array of numeric links to every preceeding input value
      linkage: 1.5,  // the linkage between this cluster and the last value to merge into it
      cluster: []    // an array of input values
    }
  

• distance

Specifies the function to use for measuring the distance between each input.

• "euclidean"

• "manhattan"

• "max"

• function (a, b)

  A custom distance function that compares input value A to input value B and returns a number (usually between 0 and 1).

levels

Agglo will return an array of inputs.length - 1 levels. The first level represents the first two clusters that were merged. The last level represents the last two clusters that were merged.

[
  { // level 1
    linkage: 2,
    source: {
      index: 0,
      value: [5, 13]
    },
    target: {
      index: 2,
      value: [6, 12]
    },
    clusters: [
      [[9, 22]],
      [[5, 13], [6, 12]],
      ...
    ]
  },
  ...
]

levels.fit(regression, callback)