Data structure introduction presentation

EE 2204 - Data Structures
and Algorithms
N Radhakrishnan
Assistant Professor
Anna University, Chennai

October 19, 2024
October 19, 2024 Anna University, Chennai - 600 025
Anna University, Chennai - 600 025 2
2
Topics
 Types of Data Structures
 Examples for each type
 Tree Data Structure
 Basic Terminology
 Tree ADT
 Traversal Algorithms
 Binary Trees
• Binary Tree Representations
• Binary Tree ADT
• Binary Tree Traversals

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Types of Data Structure

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Linear Data Structures
 In linear data structure, member elements
form a sequence. Such linear structures can
be represented in memory by using one of
the two basic strategies
 By having the linear relationship between
the elements represented by means of
sequential memory locations. These linear
structures are called arrays. By having
relationship between the elements
represented by pointers. These structures
are called linked lists.

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When to use them
 Arrays are useful when number of
elements to be stored is fixed.
 Operations like traversal searching and
sorting can easily be performed on arrays.
 On the other hand, linked lists are useful
when the number of data items in the
collection are likely to change.

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Nonlinear Data Structures
 In nonlinear data structures, data elements
are not organized in a sequential fashion. A
data item in a nonlinear data structure could
be attached to several other data elements
to reflect a special relationship among them
and all the data items cannot be traversed in
a single run.
 Data structures like multidimensional arrays,
trees and graphs are some examples of
widely used nonlinear data structures.

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Examples
 A Multidimensional Array is simply a
collection of one-dimensional arrays.
 A Tree is a data structure that is made up of
a set of linked nodes, which can be used to
represent a hierarchical relationship among
data elements.
 A Graph is a data structure that is made up
of a finite set of edges and vertices. Edges
represent connections or relationships among
vertices that stores data elements.

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Difference
 Main difference between linear and nonlinear
data structures lie in the way they organize
data elements.
 In linear data structures, data elements are
organized sequentially and therefore they are
easy to implement in the computer’s
memory.
 In nonlinear data structures, a data element
can be attached to several other data
elements to represent specific relationships
that exist among them.

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Difficult to Implement
 Due to this nonlinear structure, they might
be difficult to implement in computer’s linear
memory compared to implementing linear
data structures.
 Selecting one data structure type over the
other should be done carefully by considering
the relationship among the data elements
that needs to be stored.

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A Specific Example
 Imagine that you are hired by company XYZ
to organize all of their records into a
computer database.
 The first thing you are asked to do is create
a database of names with all the company's
management and employees.
 To start your work, you make a list of
everyone in the company along with their
position and other details.

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Employees Table

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Disadvantages of Tables
 But this list only shows one view of the
company. You also want your database to
represent the relationships between
management and employees at XYZ.
 Although your list contains both name and
position, it does not tell you which managers
are responsible for which workers and so on.
 After thinking about the problem for a while,
you decide that a tree diagram is a much
better structure for showing the work
relationships at XYZ.

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Better Representation

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Comparison
 These two diagrams are examples of
different data structures.
 In one of the data structures, your data is
organized into a list. This is very useful for
keeping the names of the employees in
alphabetical order so that we can locate the
employee's record very quickly.
 However, this structure is not very useful for
showing the relationships between
employees. A tree structure is much better
suited for this purpose.

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Tree Data Structure
 A Tree is a nonlinear data structure that
models a hierarchical organization.
 The characteristic features are that each
element may have several successors
(called its “children”) and every element
except one (called the “root”) has a unique
predecessor (called its “parent”).
 Trees are common in computer science:
Computer file systems are trees, the
inheritance structure for C++/Java classes
is a tree.

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What is a Tree ?
 In Computer Science, a tree is an
abstract model of a hierarchical structure
 A tree consists of nodes with a parent-
child relation
 Applications:
• Organization Charts
• File Systems
• Programming Environment

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Tree Definition
 Here is the recursive definition of an
(unordered) tree:
• A tree is a pair (r, S), where r is a node and S is
a set of disjoint trees, none of which contains r.
 The node r is called the root of the tree T,
and the elements of the set S are called its
subtrees.
 The set S, of course, may be empty.
 The elements of a tree are called its nodes.

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Root, Parent and Children
 If T = (x, S) is a tree, then
• x is the root of T and
• S is its set of subtrees S = {T1, T2, T3, . . ., Tn}.
 Each subtree Tj is itself a tree with its own root
rj .
 In this case, we call the node r the parent of
each node rj, and we call the rj the children of
r. In general.

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Basic Terminology
 A node with no children is called a leaf.
A node with at least one child is called
an internal node.
 The Node having further sub-branches
is called parent node.
 Every node c other than the root is
connected by an edge to some one
other node p called the parent of c.
 We also call c a child of p.

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An Example
 n1 is the parent of n2 ,n3
and n4, while n2 is the
parent of n5 and n6. Said
another way, n2, n3, and n4
are children of n1, while n5
and n6 are children of n2.

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Connected Tree
 A tree is connected in the sense that if we
start at any node n other than the root,
move to the parent of n, to the parent of the
parent of n, and so on, we eventually reach
the root of the tree.
 For instance, starting at n7, we move to its
parent, n4, and from there to n4’s parent,
which is the root, n1.

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Ancestors and Descendants
 The parent-child relationship can be
extended naturally to ancestors and
descendants.
 Informally, the ancestors of a node are
found by following the unique path from
the node to its parent, to its parent’s
parent, and so on.
 The descendant relationship is the inverse
of the ancestor relationship, just as the
parent and child relationships are
inverses of each other.

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Path and Path Length
 More formally, suppose m1,m2, . . . ,mk is a
sequence of nodes in a tree such that m1 is
the parent of m2, which is the parent of m3,
and so on, down to mk−1, which is the parent
of mk. Then m1,m2, . . . ,mk is called a path
from m1 to mk in the tree. The path length
length or
length of the path is k −1, one less than the
number of nodes on the path.

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In our Example
 n1, n2, n6 is a path of length 2 from the root
n1 to the node n6.

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Height and Depth
 In a tree, the height of a node n is the length
of a longest path from n to a leaf. The height
of the tree is the height of the root.
 The depth, or level, of a node n is the length
of the path from the root to n.

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In our Example
 node n1 has height 2, n2 has height 1, and leaf n3
has height 0. In fact, any leaf has height 0. The
height of the tree is 2. The depth of n1 is 0, the
depth of n2 is 1, and the depth of n5 is 2.

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Other Terms
 The size of a tree is the number of nodes it
contains.
 The total number of subtrees attached to
that node is called the degree of the node.
 Degree of the Tree is nothing but the
maximum degree in the tree.
 The nodes with common parent are called
siblings or brothers.

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Degree

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Degree of the Tree

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Siblings

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Terminology Explained

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Another Example

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Subtrees

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Binary Tree
 A binary tree is a tree in which no node can
have more than two subtrees. In other
words, a node can have zero, one or two
subtrees.

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Tree ADT
 The tree ADT stores elements at positions,
which are defined relative to neighboring
positions.
 Positions in a tree are its nodes, and the
neighboring positions satisfy the parent-
child relationships that define a valid tree.
 Tree nodes may store arbitrary objects.

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Methods of a Tree ADT
 As with a list position, a position object
for a tree supports the method:
element() : that returns the object
stored at this position (or node).
 The tree ADT supports four types of
methods:
• Accessor Methods
• Generic Methods
• Query Methods
• Update Methods

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Accessor Methods
 We use positions to abstract nodes. The real
power of node positions in a tree comes from
the accessor methods of the tree ADT that
return and accept positions, such as the
following:
• root(): Return the position of the tree’s root; an
error occurs if the tree is empty.
• parent(p): Return the position of the parent of p;
an error occurs if p is the root.
• children(p): Return an iterable collection
containing the children of node p.
 Iterable - you can step through (i.e. iterate) the object
as a collection

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Make a note of it
 If a tree T is ordered, then the iterable
collection, children(p), stores the children of
p in their linear order.
 If p is an external node, then children(p) is
empty.
 Any method that takes a position as
argument should generate an error condition
if that position is invalid.

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Generic Methods
 Tree ADT also supports the following Generic
Methods:
• size(): Return the number of nodes in the tree.
• isEmpty(): Test whether the tree has any nodes
or not.
• Iterator(): Return an iterator of all the elements
stored at nodes of the tree.
• positions(): Return an iterable collection of all the
nodes of the tree.

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Query Methods
 In addition to the above fundamental
accessor methods, the tree ADT also
supports the following Boolean query
methods:
• isInternal(p): Test whether node p is an internal
node
• isExternal(p): Test whether node p is an external
node
• isRoot(p): Test whether node p is the root node
(These methods make programming with tree easier and
more readable, since we can use them in the conditionals of
if -statements and while -loops, rather than using a non-
intuitive conditional).

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Update Methods
 The tree ADT also supports the following
update method:
• replace(p, e): Replace with e and return the
element stored at node p.
(Additional update methods may be defined by
data structures implementing the tree ADT)

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Tree ADT Exceptions
 An interface for the tree ADT uses the
following exceptions to indicate error
conditions:
• InvalidPositionException: This error condition may
be thrown by any method taking a position as an
argument to indicate that the position is invalid.
• BoundaryViolationException: This error condition
may be thrown by method parent() if it’s called
on the root.
• EmptyTreeException: This error condition may be
thrown by method root() if it’s called on an empty
tree.

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Traversal Algorithms
 A traversal (circumnavigation) algorithm is a
method for processing a data structure that
applies a given operation to each element of
the structure.
 For example, if the operation is to print the
contents of the element, then the traversal
would print every element in the structure.
 The process of applying the operation to an
element is called visiting the element. So
executing the traversal algorithm causes
each element in the structure to be visited.

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Level Order Traversal
 The level order traversal algorithm visits the
root, then visits each element on the first
level, then visits each element on the second
level, and so forth, each time visiting all the
elements on one level before going down to
the next level.
 If the tree is drawn in the usual manner with
its root at the top and leaves near the
bottom, then the level order pattern is the
same left-to-right top-to-bottom pattern that
you follow to read English text.

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Level Order Example

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Level order Traversal - Algorithm
 To traverse a nonempty ordered tree:
1. Initialize a queue.
2. Enqueue the root.
3. Repeat steps 4–6 until the queue is empty.
4. Dequeue node x from the queue.
5. Visit x.
6. Enqueue all the children of x in order.

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Preorder Traversal
The preorder traversal of the tree shown above would visit
the nodes in this order: a, b, e, h, i, f, c, d, g, j, k, l, m.
Note that the preorder traversal of a tree can be obtained
by circumnavigating the tree, beginning at the root and
visiting each node the first time it is encountered on the
left.

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Preorder Traversal - Algorithm
 To traverse a nonempty ordered tree:
1. Visit the root.
2. Do a recursive preorder traversal of each subtree
in order.
 The postorder traversal algorithm does a
postorder traversal recursively to each
subtree before visiting the root.

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Binary Trees
 A binary tree is either the empty set or a
triple T = (x, L, R), where x is a node and L
and R are disjoint binary trees, neither of
which contains x.
 The node x is called the root of the tree T,
and the subtrees L and R are called the left
subtree and the right subtree of T rooted at
x.

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Binary Tree

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Terminologies
 The definitions of the terms size, path,
length of a path, depth of a node, level,
height, interior node, ancestor, descendant,
and subtree are the same for binary trees as
for general trees.

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Trees and Binary Trees - Difference
 It is important to understand that while
binary trees require us to distinguish
whether a child is either a left child or a right
child, ordinary trees require no such
distinction.
 There is another technical difference. While
trees are defined to have at least one node,
it is convenient to include the empty tree,
the tree Empty tree with no nodes, among
the binary trees.

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Difference Continued
 That is, binary trees are not just trees all of whose nodes have
two or fewer children.
 Not only are the two trees in the above Figure are different
from each other, but they have no relation to the ordinary tree
consisting of a root and a single child of the root:

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Five binary trees with three nodes

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Full and Complete Binary Trees
 A binary tree is said to be full if all its leaves
are at the same level and every interior node
has two children.
 A complete binary tree is either a full binary
tree or one that is full except for a segment
of missing leaves on the right side of the
bottom level.

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Full and Complete Binary Trees

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Full Binary Tree

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Complete Binary Tree

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Full Binary Trees
 The full binary tree of height h has l = 2h
leaves and m = 2h – 1 internal nodes.
 The full binary tree of height h has a total of
n = 2h+1 – 1 nodes.
 The full binary tree with n nodes has height h
= lg(n+1) – 1.

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Complete Binary Tree
 In a complete binary tree of height h,
h + 1 ≤ n ≤ 2h+1 – 1 and h = └ lg n ┘

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Make a Note
 Complete binary trees are important because
they have a simple and natural
implementation using ordinary arrays.
 The natural mapping is actually defined for
any binary tree: Assign the number 1 to the
root; for any node, if i is its number, then
assign 2i to its left child and 2i+1 to its right
child (if they exist).
 This assigns a unique positive integer to each
node. Then simply store the element at node
i in a[i], where a[] is an array.

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Binary Tree Representations
 If a complete binary tree with n nodes (depth
= log n + 1) is represented sequentially,
then for any node with index i, 1 ≤ i ≤ n, we
have:
• parent(i) is at i/2 if i!=1. If i=1, i is at the root
and has no parent.
• leftChild(i) is at 2i if 2i ≤ n. If 2i > n, then i has
noleft child.
• rightChild(i) is at 2i+1 if 2i +1 ≤ n. If 2i +1 >n,
then i has no right child.

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Array Implementation

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The Disadvantage
 Figure above shows the incomplete binary tree and the natural
mapping of its nodes into an array which leaves some gaps.

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Linked List Implementation
typedef struct tnode *ptnode;
typedef struct tnode
{
int data;
ptnode left, right;
};

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Include One more Pointer
 A natural way to implement a tree T is to
use a linked structure, where we
represent each node p of T by a position
object with the following fields:
 A link to the parent of p, A link to the
LeftChild named Left, a link to the RightChild
named Right and the Data.
Parent
Data
Left Right

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Array Implementation
 Advantages
• Direct Access
• Finding the Parent / Children is fast
 Disadvantages
• Wastage of memory
• Insertion and Deletion will be costlier
• Array size and depth

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 Advantages
• No wastage of memory
• Insertion and Deletion will be easy
 Disadvantages
• Does not provide direct access
• Additional space in each node.

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Binary Tree ADT
 The Binary Tree ADT extends the Tree ADT,
i.e., it inherits all the methods of the Tree
ADT, in addition to that it supports the
following additional accessor methods:
• position left(p): return the left child of p, an error
condition occurs if p has no left child.
• position right(p): return the right child of p, an
error condition occurs if p has no right child.
• boolean hasLeft(p): test whether p has a left child
• boolean hasRight(p): test whether p has a right
child

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Binary Tree ADT (Cont.)
 Update methods may be defined by data
structures implementing the Binary Tree
ADT.
 Since Binary trees are ordered trees, the
iterable collection returned by method
chilrden(p) (inherited from the Tree ADT),
stores the left child of p before the right child
of p.

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Binary Tree Traversals
 The three traversal algorithms that are used
for general trees (see Chapter 10) apply to
binary trees as well: the preorder traversal,
the postorder traversal, and the level order
traversal.
 In addition, binary trees support a fourth
traversal algorithm: the inorder traversal.
These four traversal algorithms are given
next.

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Level Order Traversal
 To traverse a nonempty binary tree:
1. Initialize a queue.
2. Enqueue the root.
3. Repeat steps 4–7 until the queue is empty.
4. Dequeue a node x from the queue.
5. Visit x.
6. Enqueue the left child of x if it exists.
7. Enqueue the right child of x if it exists.

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Level Order

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Preorder Traversal
1. Visit the root.
2. If the left subtree is nonempty, do a preorder
traversal on it.
3. If the right subtree is nonempty, do a preorder
traversal on it.

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Preorder

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Postorder Traversal
1. If the left subtree is nonempty, do a
postorder traversal on it.
2. If the right subtree is nonempty, do a
postorder traversal on it.
3. Visit the root.

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Postorder

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Inorder Traversal
1. If the left subtree is nonempty, do a
preorder traversal on it.
2. Visit the root.
3. If the right subtree is nonempty, do a
preorder traversal on it.

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Inorder

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Traversal Using Flag
 The order in which the nodes are visited during a
tree traversal can be easily determined by imagining
there is a “flag” attached to each node, as follows:
 To traverse the tree, collect the flags:

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Inorder and Postorder

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Interaction

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