k_means_clustering.c File Reference

K Means Clustering Algorithm implemented. More...

#include <float.h>
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <time.h>

Include dependency graph for k_means_clustering.c:

Data Structures
struct	observation
struct	cluster

Macros
#define	_USE_MATH_DEFINES   /* required for MS Visual C */

Typedefs
typedef struct observation	observation
typedef struct cluster	cluster

Functions
int	calculateNearst (observation *o, cluster clusters[], int k)
void	calculateCentroid (observation observations[], size_t size, cluster *centroid)
cluster *	kMeans (observation observations[], size_t size, int k)
void	printEPS (observation pts[], size_t len, cluster cent[], int k)
static void	test ()
void	test2 ()
int	main ()

Detailed Description

K Means Clustering Algorithm implemented.

This file has K Means algorithm implemmented It prints test output in eps format

Note: Though the code for clustering works for all the 2D data points and can be extended for any size vector by making the required changes, but note that the output method i.e. printEPS is only good for polar data points i.e. in a circle and both test use the same.

Author

Lakhan Nad

Function Documentation

test2()

test2

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)

A function to test the kMeans function Generates 1000000 points in a circle of radius 20.0 with center at (0,0) and cluster them into 11 clusters

             {
  size_t size = 1000000L;
  observation* observations = (observation*)malloc(sizeof(observation) * size);
  double maxRadius = 20.00;
  double radius = 0;
  double ang = 0;
  size_t i = 0;
  for (; i < size; i++) {
    radius = maxRadius * ((double)rand() / RAND_MAX);
    ang = 2 * M_PI * ((double)rand() / RAND_MAX);
    observations[i].x = radius * cos(ang);
    observations[i].y = radius * sin(ang);
  }
  int k = 11;  // No of clusters
  cluster* clusters = kMeans(observations, size, k);
  printEPS(observations, size, clusters, k);
  // Free the accquired memory
  free(observations);
  free(clusters);
}