skmeans
Super fast simple k-means and k-means++ implementation for unidimiensional and multidimensional data. Works on nodejs and browser.
Installation
npm install skmeans
Usage
NodeJS
const skmeans = ; var data = 1121342521223145112232415;var res = ;Browser
Results
it: 2 k: 3 idxs: 2 0 0 2 1 1 1 2 0 2 0 2 1 1 0 centroids: 13 23 3 API
skmeans(data,k,[centroids],[iterations])
Calculates unidimiensional and multidimensional k-means clustering on data. Parameters are:
- data Unidimiensional or multidimensional array of values to be clustered. for unidimiensional data, takes the form of a simple array [1,2,3.....,n]. For multidimensional data, takes a NxM array [[1,2],[2,3]....[n,m]]
- k Number of clusters
- centroids Optional. Initial centroid values. If not provided, the algorith will try to choose an apropiate ones. Alternative values can be:
- "kmrand" Cluster initialization will be random, but with extra checking, so there will no be two equal initial centroids.
- "kmpp" The algorythm will use the k-means++ cluster initialization method.
- iterations Optional. Maximum number of iterations. If not provided, it will be set to 10000.
- distance function Optional. Custom distance function. Takes two points as arguments and returns a scalar number.
The function will return an object with the following data:
- it The number of iterations performed until the algorithm has converged
- k The cluster size
- centroids The value for each centroid of the cluster
- idxs The index to the centroid corresponding to each value of the data array
- test Function to test new point membership
Examples
// k-means with 3 clusters. Random initializationvar res = ; // k-means with 3 clusters. Initial centroids providedvar res = ; // k-means with 3 clusters. k-means++ cluster initializationvar res = ; // k-means with 3 clusters. Random initialization. 10 max iterationsvar res = ; // k-means with 3 clusters. Custom distance functionvar res = ; // Test new pointvar res = ;res; // Test new point with custom distancevar res = ;res;