Implementing K-Means Clustering in JavaScript

Introduction

In this lab, we will explore the k-means clustering algorithm in JavaScript. The purpose of this lab is to learn how to group data into k clusters based on similarity, using the k-means algorithm. We will implement the algorithm step-by-step and apply it to a sample dataset to understand how it works.

K-Means Clustering Algorithm Implementation in JavaScript

To start practicing coding using the k-means clustering algorithm, open the Terminal/SSH and type node. This algorithm groups the given data into k clusters, using the k-means clustering algorithm.

The following steps are used in the implementation:

1. Initialize appropriate variables for the cluster centroids, distances and classes using Array.from() and Array.prototype.slice().
2. Repeat the assignment and update steps using a while loop as long as there are changes in the previous iteration, as indicated by itr.
3. Calculate the euclidean distance between each data point and centroid using Math.hypot(), Object.keys() and Array.prototype.map().
4. Find the closest centroid using Array.prototype.indexOf() and Math.min().
5. Calculate the new centroids using Array.from(), Array.prototype.reduce(), parseFloat() and Number.prototype.toFixed().

const kMeans = (data, k = 1) => {
  const centroids = data.slice(0, k);
  const distances = Array.from({ length: data.length }, () =>
    Array.from({ length: k }, () => 0)
  );
  const classes = Array.from({ length: data.length }, () => -1);
  let itr = true;

  while (itr) {
    itr = false;

    for (let d in data) {
      for (let c = 0; c < k; c++) {
        distances[d][c] = Math.hypot(
          ...Object.keys(data[0]).map((key) => data[d][key] - centroids[c][key])
        );
      }
      const m = distances[d].indexOf(Math.min(...distances[d]));
      if (classes[d] !== m) itr = true;
      classes[d] = m;
    }

    for (let c = 0; c < k; c++) {
      centroids[c] = Array.from({ length: data[0].length }, () => 0);
      const size = data.reduce((acc, _, d) => {
        if (classes[d] === c) {
          acc++;
          for (let i in data[0]) centroids[c][i] += data[d][i];
        }
        return acc;
      }, 0);
      for (let i in data[0]) {
        centroids[c][i] = parseFloat(Number(centroids[c][i] / size).toFixed(2));
      }
    }
  }

  return classes;
};

To test the algorithm, call the kMeans() function with a data array and the desired number of clusters k. The function returns an array of class assignments for each data point.

kMeans(
  [
    [0, 0],
    [0, 1],
    [1, 3],
    [2, 0]
  ],
  2
); // [0, 1, 1, 0]

Summary

Congratulations! You have completed the K-Means Clustering lab. You can practice more labs in LabEx to improve your skills.