1
$\begingroup$

I am trying to implement the K-mean analysis with the Standard algorithm.

My implementation seems to work, but I noticed some strange behavior. If the k is close to half of the length of the list to be analyzed, I will get a set that is empty. I am not sure if it is the correct behavior.

I think the worst case is k equal to the length of the list, and each result sets has only 1 element. Empty result sets will happen if k is greater than the length of the list, but it is an invalid situation.

$\endgroup$
  • $\begingroup$ I don't know if this is an appropriate question for this forum, but I have included the R code below. $\endgroup$ – suncoolsu Aug 2 '11 at 7:59
4
$\begingroup$

The behavior you describe is perfectly correct. Using such large sizes of $K$ w.r.t. to your list length is also one of the reasons why you get empty clusters. Be wise when choosing $K$ and your initial set of centroids (which I assume you sampled from your population).

Remember also that even though K-means is an optimization problem it does not define a convex function.

Last but not least, execute your K-means runs several times with the same K and compare results so you'll get an idea about the stability of your problem.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.