A data structure is a specialized format for organizing, processing, retrieving and storing data. There are several basic and advanced types of data structures, all designed to arrange data to suit a specific purpose
data structure details of types and .pptpoonamsngr
The document defines and describes various data structures. It begins by defining data structures as representations of logical relationships between data elements. It then discusses how data structures affect program design and how algorithms are paired with appropriate data structures. The document goes on to classify data structures as primitive and non-primitive, providing examples of each. It proceeds to describe several specific non-primitive data structures in more detail, including lists, stacks, queues, trees, and graphs.
This document defines and describes different types of data structures. It begins by defining primitive data structures as basic structures directly operated on by the machine, such as integers and floats, and non-primitive data structures as more sophisticated structures derived from primitive ones, such as lists, stacks, queues, trees and graphs. It then provides examples and descriptions of common non-primitive data structures like arrays, lists, stacks, queues, trees and graphs, highlighting their key characteristics and common operations.
Introduction of data structure in short.pptmba29007
A data structure is a systematic way to organize, manage, and store data to enable efficient access and modification. Data structures are fundamental to computer science and programming because they directly impact the performance of algorithms. Choosing the right data structure can significantly improve the efficiency of an application.
The document discusses different data structures including primitive and non-primitive structures. It defines data structures as representations of logical relationships between data elements. Primitive structures like integers are directly operated on by machines while non-primitive structures like arrays, lists, stacks, queues, trees and graphs are built from primitive structures. Arrays store homogeneous data in consecutive memory locations accessed via indexes. Lists use nodes of data and pointer fields, connected in a linear fashion. Stacks and queues follow LIFO and FIFO principles respectively for insertion and removal. Trees have hierarchical relationships and graphs model physical networks with vertices and edges.
Introduction to data structure presentationsjayajadhav7
Data Structures is about how data can be stored in different structures. Algorithms is about how to solve different problems, often by searching through and manipulating data structures. Theory about Data Structures and Algorithms (DSA) helps us to use large amounts of data to solve problems efficiently.here given the introduction of data structure for basic learner who dont know anything about what is data structure can able to understand by using this presentations.also there are different types of data structure that is also categorized here.
A data structure is a specialized format for organizing, processing, retrieving and storing data. There are several basic and advanced types of data structures, all designed to arrange data to suit a specific purpose
This document provides an introduction to data structures. It defines data structures as representations of logical relationships between data elements. Data structures can be primitive, like integers and floats, or non-primitive, like lists, stacks, queues, trees and graphs. Non-primitive data structures are built from primitive structures and emphasize structuring groups of homogeneous or heterogeneous data. The document describes common data structures like arrays, lists, stacks, queues and trees, and explains their properties and implementations.
This document discusses different data structures and their characteristics. It defines data structures as ways of organizing data that consider the relationships between data elements. Data structures are divided into primitive and non-primitive categories. Primitive structures like integers are directly supported by programming languages, while non-primitive structures like linked lists, stacks, queues, trees and graphs are built from primitive types. Common operations on data structures include creation, selection, updating, searching, sorting, merging and deletion.
This document discusses data structures and algorithm efficiency. It defines data structures as representations of logical relationships between data elements. Data structures are classified as primitive (basic types like integers) and non-primitive (derived types like lists, stacks, queues, trees, graphs). The document explains various non-primitive data structures and their implementations. It also discusses measuring algorithm efficiency, including analyzing best, worst, and average cases. Asymptotic analysis using Big O notation is introduced as a machine-independent way to compare algorithm growth rates and determine asymptotic complexity classes.
This document provides an overview of the course "Data Structures and Applications" which covers various data structures like arrays, stacks, queues, trees, and graphs. It discusses primitive data structures like integers and non-primitive structures like linked lists. Operations on data structures like creation, searching, and deletion are also summarized. Common implementations of stacks and queues using arrays and pointers are mentioned.
This document provides an overview of the course "Data Structures and Applications" including the module topics, definitions, and classifications of data structures. The first module covers introduction to data structures, including definitions of primitive and non-primitive data structures, data structure operations, arrays, structures, stacks, and queues. Key concepts like dynamic memory allocation and various data structure implementations are also summarized.
This document discusses linear and non-linear data structures. Linear data structures like arrays, stacks, and queues store elements sequentially. Static linear structures like arrays have fixed sizes while dynamic structures like linked lists can grow and shrink. Non-linear structures like trees and graphs store elements in a hierarchical manner. Common abstract data types (ADTs) include stacks, queues, and lists, which define operations without specifying implementation. Lists can be implemented using arrays or linked lists.
Tijn van der Heijden is a business analyst with Deloitte. He learned about process mining during his studies in a BPM course at Eindhoven University of Technology and became fascinated with the fact that it was possible to get a process model and so much performance information out of automatically logged events of an information system.
Tijn successfully introduced process mining as a new standard to achieve continuous improvement for the Rabobank during his Master project. At his work at Deloitte, Tijn has now successfully been using this framework in client projects.
Introduction of data structure in short.pptmba29007
A data structure is a systematic way to organize, manage, and store data to enable efficient access and modification. Data structures are fundamental to computer science and programming because they directly impact the performance of algorithms. Choosing the right data structure can significantly improve the efficiency of an application.
The document discusses different data structures including primitive and non-primitive structures. It defines data structures as representations of logical relationships between data elements. Primitive structures like integers are directly operated on by machines while non-primitive structures like arrays, lists, stacks, queues, trees and graphs are built from primitive structures. Arrays store homogeneous data in consecutive memory locations accessed via indexes. Lists use nodes of data and pointer fields, connected in a linear fashion. Stacks and queues follow LIFO and FIFO principles respectively for insertion and removal. Trees have hierarchical relationships and graphs model physical networks with vertices and edges.
Introduction to data structure presentationsjayajadhav7
Data Structures is about how data can be stored in different structures. Algorithms is about how to solve different problems, often by searching through and manipulating data structures. Theory about Data Structures and Algorithms (DSA) helps us to use large amounts of data to solve problems efficiently.here given the introduction of data structure for basic learner who dont know anything about what is data structure can able to understand by using this presentations.also there are different types of data structure that is also categorized here.
A data structure is a specialized format for organizing, processing, retrieving and storing data. There are several basic and advanced types of data structures, all designed to arrange data to suit a specific purpose
This document provides an introduction to data structures. It defines data structures as representations of logical relationships between data elements. Data structures can be primitive, like integers and floats, or non-primitive, like lists, stacks, queues, trees and graphs. Non-primitive data structures are built from primitive structures and emphasize structuring groups of homogeneous or heterogeneous data. The document describes common data structures like arrays, lists, stacks, queues and trees, and explains their properties and implementations.
This document discusses different data structures and their characteristics. It defines data structures as ways of organizing data that consider the relationships between data elements. Data structures are divided into primitive and non-primitive categories. Primitive structures like integers are directly supported by programming languages, while non-primitive structures like linked lists, stacks, queues, trees and graphs are built from primitive types. Common operations on data structures include creation, selection, updating, searching, sorting, merging and deletion.
This document discusses data structures and algorithm efficiency. It defines data structures as representations of logical relationships between data elements. Data structures are classified as primitive (basic types like integers) and non-primitive (derived types like lists, stacks, queues, trees, graphs). The document explains various non-primitive data structures and their implementations. It also discusses measuring algorithm efficiency, including analyzing best, worst, and average cases. Asymptotic analysis using Big O notation is introduced as a machine-independent way to compare algorithm growth rates and determine asymptotic complexity classes.
This document provides an overview of the course "Data Structures and Applications" which covers various data structures like arrays, stacks, queues, trees, and graphs. It discusses primitive data structures like integers and non-primitive structures like linked lists. Operations on data structures like creation, searching, and deletion are also summarized. Common implementations of stacks and queues using arrays and pointers are mentioned.
This document provides an overview of the course "Data Structures and Applications" including the module topics, definitions, and classifications of data structures. The first module covers introduction to data structures, including definitions of primitive and non-primitive data structures, data structure operations, arrays, structures, stacks, and queues. Key concepts like dynamic memory allocation and various data structure implementations are also summarized.
This document discusses linear and non-linear data structures. Linear data structures like arrays, stacks, and queues store elements sequentially. Static linear structures like arrays have fixed sizes while dynamic structures like linked lists can grow and shrink. Non-linear structures like trees and graphs store elements in a hierarchical manner. Common abstract data types (ADTs) include stacks, queues, and lists, which define operations without specifying implementation. Lists can be implemented using arrays or linked lists.
Tijn van der Heijden is a business analyst with Deloitte. He learned about process mining during his studies in a BPM course at Eindhoven University of Technology and became fascinated with the fact that it was possible to get a process model and so much performance information out of automatically logged events of an information system.
Tijn successfully introduced process mining as a new standard to achieve continuous improvement for the Rabobank during his Master project. At his work at Deloitte, Tijn has now successfully been using this framework in client projects.
Just-in-time: Repetitive production system in which processing and movement of materials and goods occur just as they are needed, usually in small batches
JIT is characteristic of lean production systems
JIT operates with very little “fat”
Lalit Wangikar, a partner at CKM Advisors, is an experienced strategic consultant and analytics expert. He started looking for data driven ways of conducting process discovery workshops. When he read about process mining the first time around, about 2 years ago, the first feeling was: “I wish I knew of this while doing the last several projects!".
Interviews are subject to all the whims human recollection is subject to: specifically, recency, simplification and self preservation. Interview-based process discovery, therefore, leaves out a lot of “outliers” that usually end up being one of the biggest opportunity area. Process mining, in contrast, provides an unbiased, fact-based, and a very comprehensive understanding of actual process execution.
Decision Trees in Artificial-Intelligence.pdfSaikat Basu
Have you heard of something called 'Decision Tree'? It's a simple concept which you can use in life to make decisions. Believe you me, AI also uses it.
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2. INTRODUCTION
That means, algorithm is a set of instruction written to carry
out certain tasks & the data structure is the way of
organizing the data with their logical relationship retained.
To develop a program of an algorithm, we should select an
appropriate data structure for that algorithm.
Therefore algorithm and its associated data structures from a
program.
3. CLASSIFICATION OF DATA
STRUCTURE
Data structure are normally divided into two broad
categories:
Primitive Data Structure
Non-Primitive Data Structure
6. PRIMITIVE DATA STRUCTURE
There are basic structures and directly operated upon by
the machine instructions.
In general, there are different representation on different
computers.
Integer, Floating-point number, Character constants,
string constants, pointers etc, fall in this category.
7. NON-PRIMITIVE DATA STRUCTURE
There are more sophisticated data structures.
These are derived from the primitive data structures.
The non-primitive data structures emphasize on
structuring of a group of homogeneous (same type) or
heterogeneous (different type) data items.
8. NON-PRIMITIVE DATA STRUCTURE
Lists, Stack, Queue, Tree, Graph are example of non-
primitive data structures.
The design of an efficient data structure must take
operations to be performed on the data structure.
9. NON-PRIMITIVE DATA STRUCTURE
The most commonly used operation on data structure are
broadly categorized into following types:
Create
Selection
Updating
Searching
Sorting
Merging
Destroy or Delete
10. DIFFERENT BETWEEN THEM
A primitive data structure is generally a basic structure
that is usually built into the language, such as an integer,
a float.
A non-primitive data structure is built out of primitive
data structures linked together in meaningful ways, such
as a or a linked-list, binary search tree, AVL Tree, graph
etc.
11. DESCRIPTION OF VARIOUS
DATA STRUCTURES : ARRAYS
An array is defined as a set of finite number of
homogeneous elements or same data items.
It means an array can contain one type of data only,
either all integer, all float-point number or all character.
12. ARRAYS
Simply, declaration of array is as follows:
int arr[10]
Where int specifies the data type or type of elements arrays
stores.
“arr” is the name of array & the number specified inside the
square brackets is the number of elements an array can store,
this is also called sized or length of array.
13. ARRAYS
Following are some of the concepts to be remembered
about arrays:
The individual element of an array can
be accessed by specifying name of the
array, following by index or subscript
inside square brackets.
The first element of the array has index
zero[0]. It means the first element and
last element will be specified as:arr[0] &
arr[9]
Respectively.
14. ARRAYS
The elements of array will always be stored
in the consecutive (continues) memory
location.
The number of elements that can be stored
in an array, that is the size of array or its
length is given by the following equation:
(Upperbound-lowerbound)+1
15. ARRAYS
For the above array it would be
(9-0)+1=10,where 0 is the lower bound
of array and 9 is the upper bound of
array.
Array can always be read or written
through loop. If we read a one-
dimensional array it require one loop for
reading and other for writing the array.
16. ARRAYS
For example: Reading an array
For(i=0;i<=9;i++)
scanf(“%d”,&arr[i]);
For example: Writing an array
For(i=0;i<=9;i++)
printf(“%d”,arr[i]);
17. ARRAYS
If we are reading or writing two-
dimensional array it would require two
loops. And similarly the array of a N
dimension would required N loops.
Some common operation performed on
array are:
Creation of an array
Traversing an array
18. ARRAYS
Insertion of new element
Deletion of required element
Modification of an element
Merging of arrays
19. LISTS
A lists (Linear linked list) can be defined as a collection of
variable number of data items.
Lists are the most commonly used non-primitive data
structures.
An element of list must contain at least two fields, one for
storing data or information and other for storing address of
next element.
As you know for storing address we have a special data
structure of list the address must be pointer type.
20. LISTS
Technically each such element is referred to as a node,
therefore a list can be defined as a collection of nodes as
show bellow:
Head
AAA BBB CCC
Information field Pointer field
[Linear Liked List]
21. LISTS
Types of linked lists:
Single linked list
Doubly linked list
Single circular linked list
Doubly circular linked list
22. STACK
A stack is also an ordered collection of elements like
arrays, but it has a special feature that deletion and
insertion of elements can be done only from one end
called the top of the stack (TOP)
Due to this property it is also called as last in first out
type of data structure (LIFO).
23. STACK
It could be through of just like a stack of plates placed on table in
a party, a guest always takes off a fresh plate from the top and the
new plates are placed on to the stack at the top.
It is a non-primitive data structure.
When an element is inserted into a stack or removed from the
stack, its base remains fixed where the top of stack changes.
24. STACK
Insertion of element into stack is called PUSH and
deletion of element from stack is called POP.
The bellow show figure how the operations take place on
a stack:
PUSH POP
[STACK]
25. STACK
The stack can be implemented into two ways:
Using arrays (Static implementation)
Using pointer (Dynamic
implementation)
26. QUEUE
Queue are first in first out type of data structure (i.e. FIFO)
In a queue new elements are added to the queue from one end
called REAR end and the element are always removed from
other end called the FRONT end.
The people standing in a railway reservation row are an
example of queue.
27. QUEUE
Each new person comes and stands at the end of the row
and person getting their reservation confirmed get out of
the row from the front end.
The bellow show figure how the operations take place on
a stack:
10 20 30 40 50
front rear
28. QUEUE
The queue can be implemented into two ways:
Using arrays (Static implementation)
Using pointer (Dynamic
implementation)
29. TREES
A tree can be defined as finite set of data items (nodes).
Tree is non-linear type of data structure in which data
items are arranged or stored in a sorted sequence.
Tree represent the hierarchical relationship between
various elements.
30. TREES
In trees:
There is a special data item at the top of hierarchy called the
Root of the tree.
The remaining data items are partitioned into number of
mutually exclusive subset, each of which is itself, a tree
which is called the sub tree.
The tree always grows in length towards bottom in data
structures, unlike natural trees which grows upwards.
31. TREES
The tree structure organizes the data into branches,
which related the information.
A
B C
D E F G
root
32. GRAPH
Graph is a mathematical non-linear data structure
capable of representing many kind of physical structures.
It has found application in Geography, Chemistry and
Engineering sciences.
Definition: A graph G(V,E) is a set of vertices V and a set
of edges E.
33. GRAPH
An edge connects a pair of vertices and many have
weight such as length, cost and another measuring
instrument for according the graph.
Vertices on the graph are shown as point or circles and
edges are drawn as arcs or line segment.