Consider partitioning clustering and the following constraint on clusters: The number of objects in each cluster must be between \(\frac{n}{k}(1-\delta)\) and \(\frac{n}{k}(1+\delta)\), where \(n\) is the total number of objects in the data set, \(k\) is the number of clusters desired, and \(\delta\) in \([0,1)\) is a parameter. Can you extend the \(k\)-means method to handle this constraint? Discuss situations where the constraint is hard and soft.