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Classification of Data Structure
2023-03-17 22:09:51

Classification of Data Structure

A data structure is a way of organizing and storing data in a computer so that we can easily access and modify data efficiently. Different types of data structures are suited to different kinds of applications. Some data structures are highly specialized to specific tasks.

Data structures are used in many areas of computer science, including algorithms, databases, operating systems, and graphics. They are also used in many practical applications, such as web search engines and databases, social networks, and computer games.

Classification of Data Structures

Data structures can be broadly classified into two categories:

  • Linear Data Structures
  • Non-linear Data Structures

Linear Data Structures: Linear data structures include arrays, linked lists, stacks, and queues. These structures store data in a linear or sequential order.

Non-linear Data Structures: Non-linear data structures include trees, graphs, and hash tables. These structures store data in a non-linear or hierarchical fashion.

Additionally, data structures can also be classified based on the way they are implemented, such as static and dynamic data structures. Static data structures have a fixed size, while dynamic data structures can change their size during runtime.

Another way to classify data structures is based on the type of operations they support, such as insertion, deletion, search, and access.

For example, data structures like stack and queue are designed to support specific operations like push and pop, while data structures like trees and graphs are designed to support operations like traversal and searching.

Types of Data Structures

Some common types of data structures are as follows:

Primitive Data Structure

A primitive data structure is a basic data structure that is built into a programming language and is not implemented using other data structures. Primitive data structures are typically simple and efficient, and are used to store small amounts of data.

Following is a list of different types of primitive data structures:

  • Integers: Whole numbers, such as 1, 2, 3, etc.
  • Floating-point numbers: Numbers with decimal points, such as 1.23, 3.14, etc.
  • Characters: Single characters, such as 'a', 'b', 'c', etc.
  • Booleans: True or false values.
  • Strings: A sequence of characters, such as "hello" or "world".

These are the most basic data structures provided by all the programming languages. They are also known as "atomic" data types, because they cannot be divided into smaller parts.

It is worth noting that some programming languages considers few more as primitive data structures such as Enums, Structures, etc.

Non-primitive Data Structures

Non-primitive data structures are data structures that are built using one or more primitive data structures. These data structures are typically more complex and are used to store larger amounts of data.

Following is a list of different types of non-primitive data structures:

  • Arrays: A collection of elements, all of the same data types stored in contiguous memory locations.
  • Linked Lists: A collection of elements where each element points to the next element.
  • Stacks: A last-in, first-out (LIFO) collection of elements.
  • Queues: A first-in, first-out (FIFO) collection of elements.
  • Trees: A hierarchical data structure with a root node and one or more child nodes.
  • Graphs: A collection of nodes and edges, representing relationships between the nodes.
  • Hash Tables: A data structure that stores key-value pairs and uses a hash function to efficiently map keys to values.
  • Heaps: A specific kind of binary tree where the parent node is either smaller or larger than its children, depending on the type of heap.
  • Tries: A tree-like data structure used for efficient retrieval of data associated with keys, such as in the case of a dictionary.
  • Sets: A collection of unique elements, without any specific order.
  • Maps (also called Dictionaries): A collection of key-value pairs, where each key is unique.

These data structures are not built-in to the language, but can be implemented using the primitive data structures and other algorithms. They are also known as "aggregate" or "composite" data types, because they are made up of smaller parts.

Advantages of Data Structures

  • Efficiency: Data structures are designed to optimize specific operations, such as search, insertion, and deletion, which can greatly improve the performance of a program.
  • Organization: Data structures provide a way to organize and store data in a logical and meaningful way, making it easier to access and modify.
  • Reusability: Many data structures are implemented as reusable classes or libraries, which can be used in multiple programs without having to re-implement them.
  • Abstraction: Data structures provide a level of abstraction, allowing the programmer to think about the problem at a higher level, rather than having to worry about the details of how the data is stored and accessed.

Disadvantages of Data Structures

  • Complexity: Some data structures can be quite complex, which can make them difficult to understand and implement.
  • Overhead: Certain data structures can introduce additional overhead, such as the need for additional memory or computation, which can negatively impact the performance of the algorithm.
  • Limited applicability: Some data structures are designed for specific types of problems, and may not be well-suited for other types of problems.
  • Space complexity: Some data structures like linked lists and trees can consume more memory space as compared to arrays, which in some cases can be a limitation.

It is worth noting that, the advantages and disadvantages of data structures are relative and can be trade-offs based on the specific problems and use cases. Choosing the appropriate data structure for a particular problem is an important aspect of good software design.