docs(tree): IMDEEPMIND-32: added notes for tree data structure

docs/data-structure-and-algorithms/data-structures/tree/binary-tree.md

@@ -4,6 +4,12 @@ sidebar_position: 2
 
 # Binary Tree
 
+:::tip[Status]
+
+This note is complete, reviewed, and considered stable.
+
+:::
+
 A **Binary Tree** is a hierarchical data structure where each node has at most **two children**, referred to as the **left child** and **right child**.
 
 ## Key Properties

docs/data-structure-and-algorithms/data-structures/tree/common-algorithms.md

@@ -4,7 +4,11 @@ sidebar_position: 9
 
 # Common Tree Algorithms
 
-<!-- markdownlint-disable MD024 -->
+:::tip[Status]
+
+This note is complete, reviewed, and considered stable.
+
+:::
 
 ## Generating Binary Tree from In-order and Post-order Traversals
 

docs/data-structure-and-algorithms/data-structures/tree/full-vs-complete-binary-tree.md

@@ -4,7 +4,11 @@ sidebar_position: 6
 
 # Full vs Complete Binary Tree
 
-<!-- markdownlint-disable MD024 -->
+:::tip[Status]
+
+This note is complete, reviewed, and considered stable.
+
+:::
 
 ## Full Binary Tree
 

docs/data-structure-and-algorithms/data-structures/tree/introduction.md

@@ -4,6 +4,12 @@ sidebar_position: 1
 
 # Introduction
 
+:::tip[Status]
+
+This note is complete, reviewed, and considered stable.
+
+:::
+
 A **tree** is a widely used abstract data structure that simulates a hierarchical tree structure, with a root value and subtrees of children, represented as a set of linked nodes. Trees are fundamental in computer science and are used in various applications such as databases, file systems, compilers, and more.
 
 ## Tree Terminology

docs/data-structure-and-algorithms/data-structures/tree/n-ary-tree.md

@@ -4,6 +4,12 @@ sidebar_position: 4
 
 # N-ary Tree
 
+:::tip[Status]
+
+This note is complete, reviewed, and considered stable.
+
+:::
+
 An **n-ary tree** is a rooted tree in which each node can have at most `n` children. This generalizes the binary tree concept to any number of children.
 
 - If `n = 2`, it’s a binary tree.

docs/data-structure-and-algorithms/data-structures/tree/search-tree/_category_.json

@@ -0,0 +1,4 @@
+{
+  "label": "Search Tree",
+  "position": 10
+}

docs/data-structure-and-algorithms/data-structures/bst/_category_.json → docs/data-structure-and-algorithms/data-structures/tree/search-tree/bst/_category_.json

@@ -1,4 +1,4 @@
 {
   "label": "Binary Search Tree",
-  "position": 8
-}
+  "position": 2
+}

docs/data-structure-and-algorithms/data-structures/bst/introduction.md → docs/data-structure-and-algorithms/data-structures/tree/search-tree/bst/introduction.md

@@ -4,6 +4,12 @@ sidebar_position: 1
 
 # Introduction
 
+:::tip[Status]
+
+This note is complete, reviewed, and considered stable.
+
+:::
+
 A **Binary Search Tree (BST)** is a type of binary tree where each node satisfies the **BST property**:
 
 - **Left Subtree**: All values in the left subtree are **less than** the node’s value.

docs/data-structure-and-algorithms/data-structures/bst/key-operations.md → docs/data-structure-and-algorithms/data-structures/tree/search-tree/bst/key-operations.md

@@ -4,6 +4,12 @@ sidebar_position: 2
 
 # Common BST Algorithms
 
+:::tip[Status]
+
+This note is complete, reviewed, and considered stable.
+
+:::
+
 A **Binary Search Tree** supports a variety of operations to insert, find, delete, construct the tree from traversal data, validate properties, and perform traversals while maintaining the BST property.
 
 ## Search in BST

docs/data-structure-and-algorithms/data-structures/tree/search-tree/introduction.md

@@ -0,0 +1,41 @@
+---
+sidebar_position: 1
+---
+
+# Introduction
+
+:::tip[Status]
+
+This note is complete, reviewed, and considered stable.
+
+:::
+
+A search tree is a tree data structure that organizes data in a way that allows efficient searching, insertion, and deletion of elements.
+
+Unlike a general tree, a search tree follows specific rules that determine where elements are stored. These rules enable algorithms to eliminate large portions of the tree during a search, making operations significantly faster than scanning every node.
+
+For example, in a well-structured search tree, finding an element may require visiting only a small fraction of the nodes, resulting in a time complexity of **O(log n)** instead of **O(n)**.
+
+Search trees are widely used in databases, file systems, compilers, search engines, caches, and many other software systems.
+
+## Why Do We Need Search Trees?
+
+Consider searching for a value in an unsorted collection of data:
+
+```text
+10, 50, 20, 70, 30, 40
+```
+
+In the worst case, every element must be examined.
+
+```text
+Search Complexity = O(n)
+```
+
+Search trees organize data according to defined rules, allowing searches to discard large portions of the structure at each step.
+
+```text
+Search Complexity = O(log n)  (for balanced search trees)
+```
+
+As the amount of data grows, this difference becomes significant.

docs/data-structure-and-algorithms/data-structures/tree/search-tree/prefix-search-tree/_category_.json

@@ -0,0 +1,4 @@
+{
+  "label": "Prefix Search Tree",
+  "position": 4
+}

docs/data-structure-and-algorithms/data-structures/tree/search-tree/prefix-search-tree/introduction.md

@@ -0,0 +1,34 @@
+---
+sidebar_position: 1
+---
+
+# Introduction
+
+:::tip[Status]
+
+This note is complete, reviewed, and considered stable.
+
+:::
+
+A Prefix Search Tree is a tree data structure designed for storing and searching strings based on their prefixes.
+
+Unlike Binary Search Trees, which organize data using value comparisons, Prefix Search Trees organize data character by character. This allows strings that share a common prefix to share the same path in the tree.
+
+For example, the words:
+
+```text
+cat
+car
+can
+```
+
+share the prefix `"ca"` and therefore share part of the same path in the tree.
+
+Prefix Search Trees are commonly used in:
+
+- Autocomplete systems
+- Spell checkers
+- Dictionaries
+- Prefix-based searches
+
+The most common Prefix Search Tree is the **Trie**.