# 7 Important Advanced Data Structures: Simplified

## Introduction

Advanced Data structures are one of the essential branches of data science which is used for storage, organization and management of data and information for efficient, easy accessibility and modification of data. They are the basic element for creating efficient and effective software design and algorithms. The knowledge of creating and designing a good data structure is vital for becoming a commendable programmer. Its scope is also increasing with the increase in new methodologies of working in information technology.

## List of advanced data structures

Advanced Data structures have grown into many manifolds. The broad categories into which advanced data structure are divided are as follows:

Let us understand advanced data structures in detail.

## 1. Primitive types

Primitive types are either a basic building block or are a built-in type support function.

a) Boolean data type– A Boolean data type is a computer-science related algorithm where there are only two possible values of the function- true and false. This data type is generally associated with conditional statements and is considered a logical data type.

b) Floating-point arithmetic– is a formulaic representation of real numbers.

c) Fixed point numbers– Here the number of digits is fixed for a real data type.

d) Other primitive data types include character, integer, reference, enumerated type.

## 2. Composite or non-primitive type

It is also known as structure or aggregate data type and can be constructed using a combination of primitive data and other composite data.

a) Array- It is a collection of elements identified by a key or array index.

b) Records- It is structured data usually in the form of rows.

c) Union- They are a collection of several representations.

## 3. Abstract data types

In this data type, the behaviour is analyzed from the point of view of the user. The further list includes the following-

a) Container- This has a collection of the variable for problem-solving.

b) List- This includes ordered values of countable values

c) Graph- It represents pictorial representation of data for better understanding and evaluation.

d)Other abstract data types include tuple, multimap, set, multiset, stack, queue, double-ended queue.

## 4. Linear Data Structures

A data structure is linear if the elements of that data structure form a linear pattern or sequence.

a) Control table- they are the tables that control the program. They don’t have specific and particular rules and can be easily modified as per convenience.

b) Image- A pictorial representation of the entire computer system which can reproduce images after they are shut.

c) Matrix- It is a rectangular tabular form of rows and columns for analyzing the data based on probability.

d) Lists- It contains a checklist of all the contents to be included in the data for having all the things in one set and it facilitates better analysis of data.

e) Other linear data structures are array, bit array, bit field, parallel array, sparse matrix, gap buffer, dope vector, circular buffer, zipper, etc.

## 5. Tree types

Tree types include the main head (parent node) and then the branches (nodes) are divided based on subcategories and are further divided. This continues until all the elements have been allocated properly in their branches.

a) Binary trees- In this tree function, there is a maximum of two sub-nodes. They are referred to as a left child and a right child.

b) Decision trees- This tree is a systematic analysis of the possible consequences, events, outcomes to display an algorithm.

c) Other tree types include B-trees, dancing trees, fusion trees, heap, Leonardo heap, beap, radix tree, suffix tree, FM-index, Spaghetti stack, rose tree, Fenwick tree, space portioning trees, interval trees, segment trees, cover trees, minimax tree, finger tree, parse trees, expression trees, weighted balanced tree, brodal queue.

## 6. Hash based structures

a) Hash list- It is a list of hashes of data blocks in a file or block for various purposes.

b) Double hashing- This is used to resolve hash collisions.

c) Other hash based structures include Ctrie, koorde, Minhash, rolling hash, dynamic perfect hash table, bloom filter, distributed hash table, count-min table, rolling hash and many more.

## 7. Graphs

A pictorial representation of data for better understanding purposes.

a) Multigraph- it is permitted to have multiple edges which may have an identity and may not have an identity.

b) Adjacency matrix- This is used for representing finite graphs.

c) Other types of graphs are hypergraph, directed graph, scene graph, directed acyclic graph, and-inverter graph and many more.

• Other

Some other types are lightmap, winged edge, quad- edge, routing edge, routing table, symbol table.

## Conclusion

Advanced data structures are required for better presentation and analysis of complex issues. Hence a detailed knowledge of the same is very much beneficial to the developers for better prospects and has a massive demand in the market. With the growing technological developments, data structures are also advancing with more varieties being added to the list.

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