I have a sample, set of outcomes of some random variable. I divide it into "clusters", using some determinate approach. One of the clusters considered to be "correct", usually it is the one that has the most number of outcomes in it, but not always. I want to mark each cluster with some "confidence level", based on the number of outcomes it has. The formulae should depend on the number of outcomes in each cluster totally, and in the current one.
Say, if the outcome numbers, for each cluster sequentially, are:
220, 31, 28, 21, then the first cluster should have very high confidence level, something close to 100%, but not exactly 100%. But if the distribution is
113, 110, 106, 103, then all the confidences should be almost equal - and not even close to 100% - since there are several similar clusters, we cannot mark each of them as "probably correct".
I cannot use just relative proportion to the maximum, because it will cause highest cluster to have always exactly 100% confidence level, and it shouldn't be.
Is there some approach, or formulae, that gives such estimations? Thank you.