What are the classifications of data structures?

Data structures can be broadly classified into several categories based on their properties and the operations that can be performed on them. Here are the primary classifications:

1. Primitive Data Structures: These are the basic data structures provided by programming languages. They include:

   • Integers: Used to store whole numbers.
   • Floats: Used to store decimal numbers.
   • Characters: Used to store single characters.
   • Boolean: Used to store true or false values.

   Example: In Python, int, float, char, and bool are primitive data types.

2. Linear Data Structures: These structures store elements in a linear sequence. Each element is connected to the next via links or indices.

   • Arrays: Collections of elements identified by index or key.
   • Linked Lists: Chains of nodes where each node contains data and a reference to the next node.
   • Stacks: Linear structures that follow the Last In First Out (LIFO) principle.
   • Queues: Linear structures that follow the First In First Out (FIFO) principle.

   Example: An array in Java can store a fixed-size sequential collection of elements of the same type.

3. Non-Linear Data Structures: These structures do not store elements in a sequential manner. Instead, they have a hierarchical or network-like structure.

   • Trees: Hierarchical structures where each node has zero or more child nodes. Examples include Binary Trees, AVL Trees, and Red-Black Trees.
   • Graphs: Collections of nodes (vertices) connected by edges. They can be directed or undirected, weighted or unweighted.

   Example: A Binary Search Tree (BST) is a type of tree where each node has at most two children, and for each node, all values in the left subtree are less than the node's value, and all values in the right subtree are greater.

4. Hash Tables: These are data structures that provide efficient key-value pair storage and retrieval. They use a hash function to map keys to values.

   Example: In Python, dictionaries are implemented using hash tables, allowing for efficient lookup, insertion, and deletion of key-value pairs.

5. Sets: Collections of unique elements with no particular order. They often support operations like union, intersection, and difference.

   Example: In Python, the set data type is used to store unique elements.

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