Initialization of centroid for kmeans++ algorithm

I have applied simple kmeans algorithm for clustering of the datasets, which has dimension 1800 by 3. Now instead of kmeans, I want to initialize the centroid by using kmeans++ algorithm. For simple kmeans I have initialized the centres as per algorithm like Arbitrarily choose an initial k centers C = {c1, c2, · · · , ck}.

But for kmeans++ the initialization procedure is like that

1a. Take one center c1, chosen uniformly at random from X .

1b. Take a new center ci , choosing x ∈ X with probability (D(x)^2) /sum(D(x)^2) .

1c. Repeat Step 1b. until we have taken k centers altogether.

In particular, let D(x) denote the shortest distance from a data point to the closest center. “D2 weighting”

But, I am not understanding the “D2 weighting”.

How to calculate “D2 weighting” value for initialization.

Please provide me any advice regarding this.

Answer 1

[Image Analyst]

I believe you can use the 'Replicates' and 'Start' options of kmeans().

[AS]

ok. I will try

[AS]

How I will be using 'Start' option in code? Should I attached the code?

[Image Analyst]

Sure, attach your code and data if you are still having trouble.