Various types of Data Structure

Data structures are essential in organizing and managing data efficiently in computer science and software engineering. They can be broadly categorized based on their structure and use cases. Here’s a detailed discussion on various types of data structures:

1. Linear Data Structures

Linear data structures store data elements in a sequential manner, where each element is connected to its previous and next element.

a. Arrays

• Definition: A collection of elements, each identified by an index.
• Characteristics: Fixed size, homogeneous elements, random access, contiguous memory allocation.
• Operations: Traversal, insertion, deletion, searching, sorting.
• Example: int arr[5] = {10, 20, 30, 40, 50};

b. Linked Lists

• Definition: A collection of nodes where each node contains data and a reference (or link) to the next node in the sequence.
• Types:
  • Singly Linked List: Each node points to the next node.
  • Doubly Linked List: Each node points to both the next and previous nodes.
  • Circular Linked List: The last node points back to the first node.
• Operations: Traversal, insertion, deletion, searching.
• Example:

struct Node {

    int data;

    Node* next;

};

c. Stacks

• Definition: A collection of elements that follows the Last In First Out (LIFO) principle.
• Characteristics: Allows operations at one end only (top of the stack).
• Operations: Push (insert), pop (delete), peek (top element), isEmpty.
• Example:

stack<int> s;

s.push(10);

s.pop();

d. Queues

• Definition: A collection of elements that follows the First In First Out (FIFO) principle.
• Types:
  • Simple Queue: Elements are inserted at the rear and removed from the front.
  • Circular Queue: The last position is connected back to the first position.
  • Priority Queue: Each element is associated with a priority, and elements are served based on priority.
  • Double-Ended Queue (Deque): Insertion and deletion can occur at both ends.
• Operations: Enqueue (insert), dequeue (delete), front, rear, isEmpty.
• Example:

queue<int> q;

q.push(10);

q.pop();

2. Non-Linear Data Structures

Non-linear data structures store data elements hierarchically or in interconnected networks, where each element can connect to multiple elements.

a. Trees

• Definition: A hierarchical structure with a root node and child nodes forming a parent-child relationship.
• Types:
  • Binary Tree: Each node has at most two children.
  • Binary Search Tree (BST): A binary tree with left child nodes less than the parent and right child nodes greater than the parent.
  • AVL Tree: A self-balancing binary search tree.
  • B-Tree: A self-balancing search tree for databases and file systems.
  • Heap: A special tree-based structure satisfying the heap property.
• Operations: Insertion, deletion, traversal (in-order, pre-order, post-order).
• Example:

struct TreeNode {

    int data;

    TreeNode* left;

    TreeNode* right;

};

b. Graphs

• Definition: A collection of nodes (vertices) connected by edges.
• Types:
  • Directed Graph (Digraph): Edges have a direction.
  • Undirected Graph: Edges do not have a direction.
  • Weighted Graph: Edges have weights (values).
  • Unweighted Graph: Edges do not have weights.
• Operations: Traversal (DFS, BFS), shortest path (Dijkstra, Floyd-Warshall), minimum spanning tree (Prim, Kruskal).
• Example:

struct Graph {

    int V;

    list<int> *adj;

};

c. Hash Tables

• Definition: A data structure that maps keys to values for efficient data retrieval.
• Characteristics: Uses a hash function to compute an index into an array of buckets or slots.
• Operations: Insertion, deletion, searching.
• Example:

unordered_map<int, string> hashTable;

hashTable[1] = “value”;

3. Other Data Structures

a. Heaps

• Definition: A specialized tree-based data structure satisfying the heap property.
• Types:
  • Max-Heap: Parent nodes are greater than or equal to their children.
  • Min-Heap: Parent nodes are less than or equal to their children.
• Operations: Insertion, deletion, find-min/max, heapify.
• Example:

priority_queue<int> maxHeap;

priority_queue<int, vector<int>, greater<int>> minHeap;

b. Trie (Prefix Tree)

• Definition: A tree-like data structure used for storing dynamic sets of strings.
• Characteristics: Each node represents a character of the string.
• Operations: Insertion, searching, deletion, prefix matching.
• Example:

struct TrieNode {

    TrieNode* children[26];

    bool isEndOfWord;

};

Summary

Understanding and utilizing these various data structures is essential for solving complex problems efficiently. The choice of data structure depends on the specific requirements of the application, such as the type of data, the operations needed, and performance considerations.