C Source Codes for Implementing Linked List

The linked list can be implemented using the pointer structure in C so that we can

• add an element
• delete an element
• search for an element
• concatenate two linked list
• Invert a linked list
• Display elements in the list

//Program to demonstrate linked list operations

# include<stdio.h>
# include<conio.h>
# include "malloc.h"
struct node
{
int data;
struct node *link;
};

Implement Infix to postfix conversion using Stack C source codes

We can implement a valid infix expression to a valid postfix expression using stacks in c as shown:

//C PROGRAM TO CONVERT GIVEN VALID INFIX EXPRESSION INTO POSTFIX EXPRESSION USING STACKS.

#include<stdio.h>
#include<math.h>
#include<string.h>
#include<stdlib.h>
#define MAX 20

char stack[MAX];
int istack[MAX];
int top = -1;
char pop();
void push(char item);

Code Generator in C

The following code generates the code itself. The code seems to be simple but it's little bit tricky and is FAQ for programmers in an interview.

#include"stdio.h"

#include"conio.h"

char *s="#include%c#include%cchar *s=%c%s%c;%cvoid main()%c{%cclrscr();-%cprintf(s,10,10,34,s,34,10,10,10,10,10,10);%cgetch();%c}";

void main(){

//clrscr();

printf(s,10,10,34,s,34,10,10,10,10,10,10);

getch();

}

Binary Tree Implementation in C with Tree Traversals

We can implement the binary tree using C so that we can add a node, delete a node, and can perform different tree traversals:

 1. Preorder tree traversal

 2. Inorder tree traversal

 3. Post order tree traversal

/// ..............................................BINARY TREE OPERATIONS AND TREE TRAVERSALS

#include<stdio.h>
#include<conio.h>
#include<malloc.h>

///.................................................................DYNAMIC STRUCTURE FOR BINARY TREE

struct tree
{
     int info;
     struct tree *left_subtree;
     struct tree *right_subtree;
};

Time & Space Complexity of Basic K-means Algorithm

The basic k-means clustering algorithm is a simple algorithm that separates the given data space into different clusters based on centroids calculation using some proximity function. Using this algorithm, we first choose the k- points as initial centroids and then each point is assigned to a cluster with the closest centroid. The algorithm is formally described as follows:

Input: A data set D containing m objects (points) with n attributes in an              Euclidean space

Output: Partitioning of m objects into k-clusters C_1, C₂, C_3, …, C_k, i.e. C_i ⊂ D and C_i ∩ C_j = ᶲ  (for 1 ≤ i, j ≤ k)