CLASSIFICATION OF DATA STRUCTURE

Data structures are fundamental elements in computer science, crucial for organizing and managing data efficiently. They can be classified in various ways based on their characteristics and use cases. Here's a comprehensive classification:

1. Primitive vs. Non-Primitive Data Structures

Primitive Data Structures

These are the most basic data structures that directly operate upon the machine instructions. Examples include:

• Integers
• Floats
• Characters
• Booleans

Non-Primitive Data Structures

These are more complex data structures derived from primitive data structures. They are classified further into linear and non-linear structures.

2. Linear vs. Non-Linear Data Structures

Linear Data Structures

In these structures, elements are arranged in a sequential manner, and each element is connected to its previous and next element. Examples include:

• Arrays: A collection of elements identified by index or key.
• Linked Lists: Elements are stored in nodes, with each node pointing to the next.
• Stacks: Follows Last In First Out (LIFO) principle.
• Queues: Follows First In First Out (FIFO) principle.

Non-Linear Data Structures

These structures do not store elements sequentially. Examples include:

• Trees: A hierarchical structure with a root node and child nodes (e.g., binary trees, AVL trees).
• Graphs: Consists of nodes (vertices) connected by edges, can be directed or undirected.
• Heaps: A special tree-based structure that satisfies the heap property.

3. Static vs. Dynamic Data Structures

Static Data Structures

These have a fixed size and memory is allocated at compile time. Example:

• Arrays

Dynamic Data Structures

These can grow and shrink during runtime. Examples include:

• Linked Lists
• Stacks
• Queues

4. Homogeneous vs. Heterogeneous Data Structures

Homogeneous Data Structures

These store elements of the same type. Example:

• Arrays

Heterogeneous Data Structures

These can store elements of different types. Example:

• Structures in C (structs)
• Classes in Object-Oriented Programming (OOP)

5. Persistent vs. Ephemeral Data Structures

Persistent Data Structures

These retain their previous versions after an update. Example:

• Immutable lists or trees in functional programming languages

Ephemeral Data Structures

These do not retain their previous versions after an update. Example:

• Standard mutable lists or arrays

Summary Table