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Illustrates the program segment for Quick sort. It uses recursion.
Program 1: Quick Sort
Quicksort(A,m,n)
int A[ ],m,n
{
int i, j, k;
if m { i=m; j=n+1; k=A[m]; do do do ++i; while (A[i] < k); do --j; while (A[j] > k); if (i < j) { temp = A[i]; A[i] = A[j]; A[j] = temp; } while (i temp = A[m]; A[m] = A[j]; A[j] = temp; Quicksort(A,m,j-1); Quicksort(A,j+1,n); } The Quick sort algorithm uses the O(N Log2N) comparisons on average. The performance can be developed by keeping in mind the following points. 1. Switch to a faster sorting scheme such as insertion sort while the sublist size becomes comparatively small. 2. Employ a better dividing element in the implementations.
i=m; j=n+1; k=A[m]; do
do
++i;
while (A[i] < k);
--j;
while (A[j] > k);
if (i < j)
temp = A[i];
A[i] = A[j];
A[j] = temp;
}
while (i temp = A[m]; A[m] = A[j]; A[j] = temp; Quicksort(A,m,j-1); Quicksort(A,j+1,n); } The Quick sort algorithm uses the O(N Log2N) comparisons on average. The performance can be developed by keeping in mind the following points. 1. Switch to a faster sorting scheme such as insertion sort while the sublist size becomes comparatively small. 2. Employ a better dividing element in the implementations.
temp = A[m];
A[m] = A[j];
Quicksort(A,m,j-1);
Quicksort(A,j+1,n);
The Quick sort algorithm uses the O(N Log2N) comparisons on average. The performance can be developed by keeping in mind the following points.
1. Switch to a faster sorting scheme such as insertion sort while the sublist size becomes comparatively small.
2. Employ a better dividing element in the implementations.
I am looking for some help with a data mining class with questions that are about neural networks and decision trees. Can you help? I can send document with questions.
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