How to estimate K for K-means clustring

I'm working on unsupervised classification or clustering, i want to estimate the K (which refers to cluster number) before starting th k-means algorithm

Answer 1

[Walter Roberson]

You will probably not find any code already implemented for this purpose.

The theoretical answer for the "best" number of clusters to use is "one cluster for every unique point", as that will always have the best possible fit.

If you do not wish to use one cluster for every unique point, you need to have some kind of penalty term that favors fewer clusters. I read through the theory paper on that a few years ago, and it was clear to me that they were setting the weights arbitrarily (but usefully for the kinds of clustering they were doing), and that there was no way to calculate what the weights should be without some knowledge of the range of number of clusters that would be appropriate for the physical system being examined. The theoretical algorithms were not suitable for "unsupervised learning", only for "supervised learning". The work we were doing at the time required unsupervised learning, so there was no way for us to determine what the proper number of clusters should be.

Answer 2

[the cyclist]

This is not really a MATLAB question, but rather a general data science question.

Googling "how to choose k in k means" found this Wikipedia page on the topic (and many others) that might help you.

[wisekily]

there are several methods i want to find the implemented ones, i don't have enough time to spend on this implementation

[the cyclist]

I don't know of any methods in MATLAB to help you choose K, other than plotting results post hoc to see how different choices of K did. See, for example, this page.

[Image Analyst]

There are MATLAB functions for estimating the best k. I don't remember what they were - I'd have to look them up in the Machine Learning course notes.

[wisekily]

I'm waiting for your answer

Answer 3

[Image Analyst]

The web page on kmeans explains how you can use silhouette() to determine the best number of clusters, k:

http://www.mathworks.com/help/stats/k-means-clustering.html

[wisekily]

still looking and waiting for an answer !!

[Walter Roberson]

Did you read through the link that Image Analyst posted?

[the cyclist]

Which is also the same link that I pointed you to earlier. So, uh, now you have 3 of the top 10 contributors to this forum telling you consistently the same thing.

Answer 4

[kira]

old question, but I just found a way myself looking at matlab documentation:

klist=2:n;%the number of clusters you want to try
myfunc = @(X,K)(kmeans(X, K));
eva = evalclusters(net.IW{1},myfunc,'CalinskiHarabasz','klist',klist)
classes=kmeans(net.IW{1},eva.OptimalK);

[Bashar Saad]

could you help me pleas the code is not clear