kmpp

When dealing with lots of data points, clustering algorithms may be used to group them. The k-means algorithm partitions n data points into k clusters and finds the centroids of these clusters incrementally.

The algorithm assigns data points to the closest cluster, and the centroids of each cluster are re-calculated. These steps are repeated until the centroids do not changing anymore.

The basic k-means algorithm is initialized with k centroids at random positions. This implementation addresses some disadvantages of the arbitrary initialization method with the k-means++ algorithm (see "Further reading" at the end).

Installation

Installing via npm

Install kmpp as Node.js module via NPM:

$ npm install kmpp

Example

var kmpp = require('kmpp');
 
kmpp([
  [x1, y1, ...],
  [x2, y2, ...],
  [x3, y3, ...],
  ...
], {
  k: 4
});
 
// =>
// { converged: true,
//   centroids: [[xm1, ym1, ...], [xm2, ym2, ...], [xm3, ym3, ...]],
//   counts: [ 7, 6, 7 ],
//   assignments: [ 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1 ]
// }

API

kmpp(points[, opts)

Exectes the k-means++ algorithm on points.

Arguments:

• points (Array): An array-of-arrays containing the points in format [[x1, y1, ...], [x2, y2, ...], [x3, y3, ...], ...]
• opts: object containing configuration parameters. Parameters are
  • distance (function): Optional function that takes two points and returns the distance between them.
  • initialize (Boolean): Perform initialization. If false, uses the initial state provided in centroids and assignments. Otherwise discards any initial state and performs initialization.
  • k (Number): number of centroids. If not provided, sqrt(n / 2) is used, where n is the number of points.
  • kmpp (Boolean, default: true): If true, uses k-means++ initialization. Otherwise uses naive random assignment.
  • maxIterations (Number, default: 100): Maximum allowed number of iterations.
  • norm (Number, default: 2): L-norm used for distance computation. 1 is Manhattan norm, 2 is Euclidean norm. Ignored if distance function is provided.
  • centroids (Array): An array of centroids. If initialize is false, used as initialization for the algorithm, otherwise overwritten in-place if of the correct size.
  • assignments (Array): An array of assignments. Used for initialization, otherwise overwritten.
  • counts (Array): An output array used to avoid extra allocation. Values are discarded and overwritten.

Returns an object containing information about the centroids and point assignments. Values are:

• converged: true if the algorithm converged successfully
• centroids: a list of centroids
• counts: the number of points assigned to each respective centroid
• assignments: a list of integer assignments of each point to the respective centroid
• iterations: number of iterations used

Credits

• Jared Harkins improved the performance by reducing the amount of function calls, reverting to Manhattan distance for measurements and improved the random initialization by choosing from points

• Ricky Reusser refactored API

Further reading

• Wikipedia: k-means clustering
• Wikipedia: Determining the number of clusters in a data set
• k-means++: The advantages of careful seeding, Arthur Vassilvitskii
• k-means++: The advantages of careful seeding, Presentation by Arthur Vassilvitskii (Presentation)

License

© 2017-2019. MIT License.