A tree is a sort of data structure used in computer
science and data structures that simulates a natural tree's hierarchical
structure. Except for the root node, which has no parent node, it consists of
nodes connected by edges, each of which may have zero or more child nodes. Each
node in a tree has a value and could also have references to the nodes below
it.
Data structures that can represent data items with a
hierarchical relationship between their parents, descendants, and so on include
trees, which are particularly adaptable, versatile, and durable. In a tree,
each node can have 0–no child nodes, each of which can have its offspring. A
common motif in computer science is trees. and programming for effectively
sorting, searching, and organizing data.
A non-linear data structure called a tree is used to
organize things or elements in sorted order. It is utilized to symbolize a
hierarchical relationship between various data components. Except for the root
node, which has no parent node, every node in a tree can have zero or more
child nodes. In computer science and programming, trees are frequently used for
effective data searching, sorting, and organization.
A chart A tree is theoretically defined as One or more
data contained in a finite set. The ROOT of the tree is a specific data object.
The remaining data items are divided into several
mutually exclusive subsets, each of which is a tree; these subsets are referred
to as SUBTREES.
Tree data
structures types
General tree
Binary tree
Binary search tree
AVL tree
Red-black tree
1. General Tree
A generic tree is a tree data structure with an
unrestricted hierarchical structure.
Properties
Observe a tree's characteristics.
A node can produce as many offspring as it desires.
2.
Binary Tree
The following characteristics of a binary tree are
characteristics of a data structure.
Properties
Observe a tree's characteristics.
There are a maximum of two children per node.
The left and right child nodes of this pair are
referred to as such.
Usage
They are built by compilers using syntax trees.
This class is utilized to implement expression parsers
and solvers.
In this are stored router tables.
3.
Binary Search Tree
A binary tree is condensed into a binary search tree.
Properties
1. Examine the characteristics of a binary tree.
2. The attribute of the binary-search tree is unique.
This property indicates that the right child's value must be greater than the
parent value or equal to it, while the left child's value must be lower than
the parent value or equal to it.
Usage
This package implements basic sorting algorithms.
It is possible to utilize them to build priority
queues.
This is often used by search apps when data is
constantly entering and exiting.
4.
AVL tree
An AVL tree is a self-balancing binary search tree. The
first tree whose height is automatically balanced is this one.
Properties
It is recommended to adhere to the properties of binary
search trees.
Self-balancing.
A statistic known as a balancing factor, which
represents the height difference between a node's left and right subtrees, is
kept track of by each node.
All nodes must have a balancing factor of -1, 0, or 1.
Rotations should be carried out to balance the tree
following insertions or deletions (self-balancing) if at least one node does
not have a balance factor of -1, 0, or 1.
Usage
This is the best approach to take when there are
numerous insertions.
It is employed in the Memory management subsystem of
the Linux kernel to examine memory regions of processes during preemption.
5.
Red-black tree
A self-balancing binary search tree called a red-black
tree has each leaf being The node colors are used to keep the tree relatively
balanced throughout insertions and deletions.
Properties
It is recommended to adhere to the properties of binary
search trees.
Self-balancing.
Each node is either red or black.
The root is usually omitted and is black.
The leaves (labeled NIL) are all entirely black.
If a node is red, both of its offspring are black.
There must always be an equal number of black nodes
between each node and each of its child nodes.
Usage
as a base for data structures for computational
geometry.
It is utilized by the Completely Fair Scheduler in
contemporary Linux kernels.
utilized in the poll system call implementation in the
Linux kernel.
Conclusion
You must first master the fundamentals of tree data
structure, including what a tree is, where you may use it, and its
characteristics. You can consult Narasimha Karumanchi's Data Structure and
Algorithm Made Easy for that. You can then go on to the issue set. You'll gain
a lot of clarity on the subject via practice.
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