Consider partitioning clustering and the following constraint on clusters: the number of objects in each cluster must be between n k(1 −δ) and n k(1 +δ), where n is the total number of objects in the data set, k is the number of clusters desired, and δ 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.

Answercomposite key;

Function tipcalculate (slider, bill){  var tip = document.getelementbyid('tipamount');   var slideval = document.getelementbyid('slideval');   var bill = document.getelementbyid(bill).value;   var prcnt = slider * .01;       if (bill == null || bill == '') {      tip.innerhtml = ' enter an amount';       return false;   }  if(isnan(bill)) {      tip.innerhtml = ' enter a number';       return false;   }  if(bill > = 0){      tip.innerhtml = '$' + (bill * prcnt) .tofixed(2);   slideval.innerhtml = slider + '%';   }}