Skip to main content

​
CMeans

Executes one iteration of the C-means clustering algorithm. This function calculates the distance matrix, membership matrix, new centroids, cost values, and total cost function for a single iteration. 
export const CMeans = (points: Point[], centroids: Point[])

​
Parameters

​
points
Point[]
required
Array of points to cluster. Each point has x and y coordinates.
​
centroids
Point[]
required
Array of initial centroids. Each centroid has x and y coordinates.

​
Returns

Returns an object containing the results of one C-means iteration:
​
distanceMatrix
number[][]
Matrix of distances where each element [i][j] is the distance between the i-th centroid and the j-th point.
​
membershipMatrix
number[][]
Membership matrix where each element [i][j] is 1 if the j-th point belongs to the i-th centroid, otherwise 0.
​
newCentroids
Point[]
New array of centroids calculated as the mean of all points belonging to each centroid.
​
costValues
number[]
Array of cost values for each centroid.
​
costFunction
number
Total cost as the sum of all individual cost values.

​
Example

import { CMeans } from './utils/CMeans';

const points = [
  { x: 1, y: 2 },
  { x: 3, y: 4 },
  { x: 5, y: 6 },
  { x: 7, y: 8 }
];

const centroids = [
  { x: 2, y: 3 },
  { x: 6, y: 7 }
];

const result = CMeans(points, centroids);

console.log(result.newCentroids);
console.log(result.costFunction);

​
Helper Functions

​
getDistanceMatrix

Calculates the distance matrix between each point and each centroid. 
export const getDistanceMatrix = (points: Point[], centroids: Point[])
​
points
Point[]
required
Array of points.
​
centroids
Point[]
required
Array of centroids.
Returns: A matrix of distances where each element [i][j] is the distance between the i-th centroid and the j-th point.

​
getMembershipMatrix

Generates the membership matrix from the distance matrix. 
export const getMembershipMatrix = (distanceMatrix: number[][])
​
distanceMatrix
number[][]
required
Matrix of distances between points and centroids.
Returns: A membership matrix where each element [i][j] is 1 if the j-th point belongs to the i-th centroid, otherwise 0.

​
getNewCentroids

Calculates new centroids based on the membership matrix. 
export const getNewCentroids = (points: Point[], membershipMatrix: number[][])
​
points
Point[]
required
Array of points.
​
membershipMatrix
number[][]
required
Membership matrix.
Returns: A new array of centroids calculated as the mean of all points belonging to each centroid.

​
getCostValues

Calculates the cost values for each centroid based on the membership and distance matrices. 
export const getCostValues = (membershipMatrix: number[][], distanceMatrix: number[][])
​
membershipMatrix
number[][]
required
Membership matrix.
​
distanceMatrix
number[][]
required
Distance matrix.
Returns: An array of cost values for each centroid.

​
getCostFunction

Calculates the total cost from an array of individual cost values. 
export const getCostFunction = (costValues: number[])
​
costValues
number[]
required
Array of individual cost values.
Returns: The total cost as the sum of all individual costs.

​
euclidianDistance

Calculates the Euclidean distance between two points. 
export const euclidianDistance = (pointA: Point, pointB: Point)
​
pointA
Point
required
First point.
​
pointB
Point
required
Second point.
Returns: The Euclidean distance between the two points.

​
zeroMatrix

Creates a zero matrix with the same dimensions as the input matrix. 
export function zeroMatrix(matrix: number[][])
​
matrix
number[][]
required
Input matrix.
Returns: A matrix of the same dimensions with all elements set to 0.

​
generateRandomPoints

Generates an array of random points. 
export function generateRandomPoints(n: number)
​
n
number
required
Number of points to generate.
Returns: An array of n points with random coordinates between -100 and 100.

​
Example

import { generateRandomPoints } from './utils/CMeans';

const randomPoints = generateRandomPoints(10);
console.log(randomPoints);
// Output: [{ x: -45, y: 67 }, { x: 23, y: -89 }, ...]

Build docs developers (and LLMs) love

Get started for freeTalk to us