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kMeans javascript Code Snippet in 2023

algorithmarrayadvanced

Last Updated: 3 February 2023

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

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