How about defining an arbitrary resolution for your space, and calculate for each point in that matrix, a measure of the distance from that point to all dots, then you could make a "heat graph" and use a threshold to define the clusters.
It's a nice exercise for processing, maybe later I will post a solution.
EDIT:
Here it is:
//load the image
PImage sample;
sample = loadImage("test.png");
size(sample.width, sample.height);
image(sample, 0, 0);
int[][] heat = new int[width][height];
//parameters
int resolution = 5; //distance between points in the gridq
int distance = 8; //distance at wich two points are considered near
float threshold = 0.5;
int level = 240; //leven to detect the dots
int sensitivity = 1; //how much does each dot matters
//calculate the "heat" on each point of the grid
color black = color(0,0,0);
loadPixels();
for(int a=0; a
EDIT 2 (slighly less inefficient code but same output):
//load the image
PImage sample;
sample = loadImage("test.png");
size(sample.width, sample.height);
image(sample, 0, 0);
int[][] heat = new int[width][height];
int dotQ = 0;
int[][] dots = new int[width*height][2];
int X = 0;
int Y = 1;
//parameters
int resolution = 1; //distance between points in the grid
int distance = 20; //distance at wich two points are considered near
float threshold = 0.6;
int level = 240; //minimum brightness to detect the dots
int sensitivity = 1; //how much does each dot matters
//detect all dots in the sample
loadPixels();
for(int x=0; x
And the output with (a reduced) Kent sample:
