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K-means clustering

Groups the given data into k clusters, using the k-means clustering algorithm.

  • Use Array.from() and Array.prototype.slice() to initialize appropriate variables for the cluster centroids, distances and classes.

  • Use a while loop to repeat the assignment and update steps as long as there are changes in the previous iteration, as indicated by itr.

  • Calculate the euclidean distance between each data point and centroid using Math.hypot(), Object.keys() and Array.prototype.map().

  • Use Array.prototype.indexOf() and Math.min() to find the closest centroid.

  • Use Array.from() and Array.prototype.reduce(), as well as parseFloat() and Number.prototype.toFixed() to calculate the new centroids.

Sample Code​

 import {kMeans} from 'vijay-js';

  

const data = kMeans([[0, 0], [0, 1], [1, 3], [2, 0]], 2);;
console.log('data :>> ', data);

Output​

data :>>  [0, 1, 1, 0]