K means clustering with initial guess centroids given

I have the problem where i have been given a 10,000x1 selection of data points, 100 points collected every weekend for 100 weeks. I am also given a 100x1 text file which i should use as the 'initial guess' for centroids. I need to iterate through the k means clustering algorithym until the distance between centroid locations is 0.00001. Please help, thanks

[Cris LaPierre]

What would you like help with? Here is the documentation for the kmeans function.

[Riordan Moloney]

[Riordan Moloney]

This is the full problem i have been set

[Image Analyst]

Looks like you're supposed to write your own kmeans function instead of using the built-in one, right?

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[Riordan Moloney]

Yes this is what i am strugggling with, I assume i will need to use implemented for loops, but the data variables are of different sizes and i am not sure how to test which value in the initial centroids is closest to the particular value i will be testing in the results data.

Answer 1

[Rishabh Mishra]

Hi,

I would like to make following assumptions:

1. The points over which you are applying k-means clustering are 2-D coordinates. The points are represented using 2 dimensions. I.e., (x,y).
2. ‘arr’ - the 10000 x 2 array of 10000 points each with 2 dimensions.
3. ‘centroid’ - the 100 x 2 of 100 centroids each with 2 dimensions.

Use the code below to perform k-means clustering on given points:

k = 100; % number of cluster 
[idx,C] = kmeans(arr,k,'Start',centroid); 
  
% idx - defines which cluster a given point is assigned to 
% C - gives co-ordinate of all the 100 cluster centroids 

Hope this helps.