# K-means clustering

JavaScript, Algorithm, Array · Dec 29, 2020

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]`