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Trees. Tree Basics. A tree is a set of nodes . A tree may be empty (i.e., contain no nodes). If not empty, there is a distinguished node, r, called the root and zero or more non-empty subtrees T 1 , T 2 , T 3 ,….T k , each of whose roots is connected by a directed edge from r.

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Tree basics

Tree Basics

  • A tree is a set of nodes.

  • A tree may be empty (i.e., contain no nodes).

  • If not empty, there is a distinguished node, r, called the root and zero or more non-empty subtrees T1, T2, T3,….Tk, each of whose roots is connected by a directed edge from r.

  • Trees are recursive in their definition and, therefore, in their implementation.

A general tree

A General Tree













Tree terminology

Tree Terminology

  • Tree terminology takes its terms from both nature and genealogy.

  • A node directly below the root, r, of a subtree is a child of r, and r is called its parent.

  • All children with the same parent are called siblings.

  • A node with one or more children is called an internal node.

  • A node with no children is called a leaf or external node.

Tree terminology con t

Tree Terminology (con’t)

  • A path in a tree is a sequence of nodes, (N1, N2, … Nk) such that Ni is the parent of Ni+1 for 1 <= i <= k.

  • The length of this path is the number of edges encountered (k – 1).

  • If there is a path from node N1 to N2, then N1 is an ancestor of N2 and N2 is a descendant of N1.

Tree terminology con t1

Tree Terminology (con’t)

  • The depth of a node is the length of the path from the root to the node.

  • The height of a node is the length of the longest path from the node to a leaf.

  • The depth of a tree is the depth of its deepest leaf.

  • The height of a tree is the height of the root.

  • True or False – The height of a tree and the depth of a tree always have the same value.

Tree storage

Tree Storage

  • First attempt - each tree node contains

    • The data being stored

      • We assume that the objects contained in the nodes support all necessary operations required by the tree.

    • Links to all of its children

  • Problem: A tree node can have an indeterminate number of children. So how many links do we define in the node?

First child next sibling

First Child, Next Sibling

  • Since we can’t know how many children a node can have, we can’t create a static data structure -- we need a dynamic one.

  • Each node will contain

    • The data which supports all necessary operations

    • A link to its first child

    • A link to a sibling

First child next sibling representation

First Child, Next Sibling Representation

  • To be supplied in class

Tree traversal

Tree Traversal

  • Traversing a tree means starting at the root and visiting each node in the tree in some orderly fashion. “visit” is a generic term that means “perform whatever operation is applicable”.

  • “Visiting” might mean

    • Print data stored in the tree

    • Check for a particular data item

    • Almost anything

Breadth first tree traversals

Breadth-First Tree Traversals

  • Start at the root.

  • Visit all the root’s children.

  • Then visit all the root’s grand-children.

  • Then visit all the roots great-grand-children, and so on.

  • This traversal goes down by levels.

  • A queue can be used to implement this algorithm.

Bf traversal pseudocode

BF Traversal Pseudocode

Create a queue, Q, to hold tree nodes

Q.enqueue (the root)

while (the queue is not empty)Node N = Q.dequeue( )for each child, X, of N Q.enqueue (X)

  • The order in which the nodes are dequeued is the BF traversal order.

Depth first traversal

Depth-First Traversal

  • Start at the root.

  • Choose a child to visit; remember those not chosen

  • Visit all of that child’s children.

  • Visit all of that child’s children’s children, and so on.

  • Repeat until all paths have been traversed

  • This traversal goes down a path until the end, then comes back and does the next path.

  • A stack can be used to implement this algorithm.

Df traversal pseudocode

DF Traversal Pseudocode

Create a stack, S, to hold tree nodes

S.push (the root)

While (the stack is not empty)Node N = S.pop ( )for each child, X, of NS.push (X)

The order in which the nodes are popped is the DF traversal order.

Performance of bf and df traversals

Performance of BF and DF Traversals

  • What is the asymptotic performance of breadth-first and depth-first traversals on a general tree?

Binary trees

Binary Trees

  • A binary tree is a tree in which each node may have at most two children and the children are designated as left and right.

  • A full binary tree is one in which each node has either two children or is a leaf.

  • A perfect binary tree is a full binary tree in which all leaves are at the same level.

A binary tree

A Binary Tree


A binary tree?

A full binary tree?


A binary tree?

A full binary tree?

A perfect binary tree?

Binary tree traversals

Binary Tree Traversals

  • Because nodes in binary trees have at most two children (left and right), we can write specialized versions of DF traversal. These are called

    • In-order traversal

    • Pre-order traversal

    • Post-order traversal

In order traversal

In-Order Traversal

  • At each node

    • visit my left child first

    • visit me

    • visit my right child last











In order traversal code

In-Order Traversal Code

void inOrderTraversal(Node *nodePtr) {

if (nodePtr != NULL) {


cout << nodePtr->data << endl;




Pre order traversal

Pre-Order Traversal

  • At each node

    • visit me first

    • visit my left child next

    • visit my right child last











Pre order traversal code

Pre-Order Traversal Code

void preOrderTraversal(Node *nodePtr) {

if (nodePtr != NULL) {

cout << nodePtr->data << endl;





Post order traversal

Post-Order Traversal

  • At each node

    • visit my left child first

    • visit my right child next

    • visit me last











Post order traversal code

Post-Order Traversal Code

void postOrderTraversal(Node *nodePtr) {

if (nodePtr != NULL) {



cout << nodePtr->data << endl;



Binary tree operations

Binary Tree Operations

  • Recall that the data stored in the nodes supports all necessary operators. We’ll refer to it as a “value” for our examples.

  • Typical operations:

    • Create an empty tree

    • Insert a new value

    • Search for a value

    • Remove a value

    • Destroy the tree

Creating an empty tree

Creating an Empty Tree

  • Set the pointer to the root node equal to NULL.

Inserting a new value

Inserting a New Value

  • The first value goes in the root node.

  • What about the second value?

  • What about subsequent values?

  • Since the tree has no properties which dictate where the values should be stored, we are at liberty to choose our own algorithm for storing the data.

Searching for a value

Searching for a Value

  • Since there is no rhyme or reason to where the values are stored, we must search the entire tree using a BF or DF traversal.

Removing a value

Removing a Value

  • Once again, since the values are not stored in any special way, we have lots of choices. Example:

    • First, find the value via BF or DF traversal.

    • Second, replace it with one of its descendants (if there are any).

Destroying the tree

Destroying the Tree

  • We have to be careful of the order in which nodes are destroyed (deallocated).

  • We have to destroy the children first, and the parent last (because the parent points to the children).

  • Which traversal (in-order, pre-order, or post-order) would be best for this algorithm?

Giving order to a binary tree

Giving Order to a Binary Tree

  • Binary trees can be made more useful if we dictate the manner in which values are stored.

  • When selecting where to insert new values, we could follow this rule:

    • “left is less”

    • “right is more”

  • Note that this assumes no duplicate nodes (i.e., data).

Binary search trees

Binary Search Trees

  • A binary tree with the additional property that at each node,

    • the value in the node’s left child is smaller than the value in the node itself, and

    • the value in the node’s right child is larger than the value in the node itself.

A binary search tree

A Binary Search Tree









Searching a bst

Searching a BST

  • Searching for the value X, given a pointer to the root

    • If the value in the root matches, we’re done.

    • If X is smaller than the value in the root, look in the root’s left subtree.

    • If X is larger than the value in the root, look in the root’s right subtree.

  • A recursive routine – what’s the base case?

Inserting a value in a bst

Inserting a Value in a BST

  • To insert value X in a BST

    • Proceed as if searching for X.

    • When the search fails, create a new node to contain X and attach it to the tree at that node.



100 38 56 150 20 40 125 138 90

Removing a value from a bst

Removing a Value From a BST

  • Non-trivial

  • Three separate cases:

    • node is a leaf (has no children)

    • node has a single child

    • node has two children



















Destroying a bst

Destroying a BST

  • The fact that the values in the nodes are in a special order doesn’t help.

  • We still have to destroy each child before destroying the parent.

  • Which traversal must we use?

Performance in a bst

Performance in a BST

  • What is the asymptotic performance of:

    • insert

    • search

    • remove

  • Is the performance of insert, search, and remove for a BST improved over that for a plain binary tree?

    • If so, why?

    • If not, why not?

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