# How to handle distribution of values with same attributes into different classes

I'm a student studying a data mining course and have come across a problem.
I need to explain the problem with the help of an example scenario as I do not know how to explain the problem in any other way.

I have n 'manufacture dates' (classes). On a particular day, y identical balls are to be segregated into these n classes based on their date of manufacture. I have x identical balls left for which I do not know the dates of manufacture (unclassified), i.e. y-x balls were classified.

Let us call the mean/standard deviation found when all balls were classified correctly to be the "true" mean/standard deviation (i.e. mean/standard deviation when all y balls are correctly classified)

Since we do not know the classes the x balls belong to, how can we distribute the x balls amongst the n classes so that we get a mean/standard deviation that is close to the true mean/standard deviation ?

My approach:

1. Case1: Value of x (i.e. number of balls not classified on a particular day) is relatively small to the total number of balls given on the same day (maybe 1 out of 10 balls are not classified).
In this case, I considered ignoring the balls that haven't been classified as the difference between the final result (mean and standard deviation of the balls classified) and the true mean/standard deviation would be minute.

2. Case2: Value of x is relatively large (i.e 4 out of 10 balls are not classified OR 6 out of 10 balls are not classified).
In this case, I do not prefer ignoring the unclassified balls as the end result (mean, standard deviation) will be erroneous. Rather, I thought of performing an even distribution or weighted distribution of the unclassified balls into the classes, but I do not have good reasoning for why this would be a good approach other than the point of not ignoring majority of the balls.

• I don't understand what you are asking. What does it mean to "handle the balls that haven't been segregated"? Can you rephrase this as an algorithmic problem, by describing the inputs and the outputs? I'm not sure what kind of answer you are hoping for or how to evaluate proposed answers. – D.W. Sep 13 '20 at 18:50
• @D.W. I made an edit to the question. Does this clarify the question ? – mahesh Rao Sep 14 '20 at 3:30
• Rather than using "Edit:", we'd prefer that you revise the question so it reads well for someone who encounters the question for the first time. There's no need to mark what has changed - we have built-in revision history for that. See cs.meta.stackexchange.com/q/657/755. – D.W. Sep 14 '20 at 5:01
• I still don't understand the question. Again, I suggest you describe this as an algorithmic task. What is the desired output? You say you want to compute the "mean and standard deviation of the balls segregated to the different classes on each day"; well, you can do that immediately, without needing to know anything about the balls that weren't segregated, as the answer doesn't depend on them. Perhaps you wanted to compute something else? – D.W. Sep 14 '20 at 5:03
• @D.W. I tried to rephrase the question. Is it clear now ? – mahesh Rao Sep 14 '20 at 11:08