The document discusses linked lists and binary search trees. It provides definitions and explanations of key concepts such as nodes, pointers, singly linked lists, doubly linked lists, and tree traversal. It also includes examples of implementing linked lists and binary search trees in C code, with functions for operations like insertion, deletion, searching, and finding the minimum/maximum elements.
Data Structure is the specific method for sorting out the data in a system with the goal that it could be utilized efficiently. These can implement at least one specific abstract data types (ADT), which indicate the operations that can be performed on the data structure and the computational unpredictability of those operations. Copy the link given below and paste it in new browser window to get more information on Data Structure & Algorithms:- www.transtutors.com/homework-help/computer-science/data-structure-and-algorithms.aspx
Tree and Binary search tree in data structure.
The complete explanation of working of trees and Binary Search Tree is given. It is discussed such a way that everyone can easily understand it. Trees have great role in the data structures.
Data Structure is the specific method for sorting out the data in a system with the goal that it could be utilized efficiently. These can implement at least one specific abstract data types (ADT), which indicate the operations that can be performed on the data structure and the computational unpredictability of those operations. Copy the link given below and paste it in new browser window to get more information on Data Structure & Algorithms:- www.transtutors.com/homework-help/computer-science/data-structure-and-algorithms.aspx
Tree and Binary search tree in data structure.
The complete explanation of working of trees and Binary Search Tree is given. It is discussed such a way that everyone can easily understand it. Trees have great role in the data structures.
In computer science, a data structure is a data organization, management, and storage format that enables efficient access and modification. More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data. https://apkleet.com
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introduction to binary tree , binary search tree, operations of binary search tree, insertion, deletions, search. 3 cases of deletions, graph tree of binary search tree, complaxcity, notations,algorithms, Data Analysis
a. Concept and Definition
b. Binary Tree
c. Introduction and application
d. Operation
e. Types of Binary Tree
• Complete
• Strictly
• Almost Complete
f. Huffman algorithm
g. Binary Search Tree
• Insertion
• Deletion
• Searching
h. Tree Traversal
• Pre-order traversal
• In-order traversal
• Post-order traversal
Slides at myblog
http://www.ashimlamichhane.com.np/2016/07/tree-slide-for-data-structure-and-algorithm/
Assignments at github
https://github.com/ashim888/dataStructureAndAlgorithm/tree/dev/Assignments/assignment_7
Presentation On Binary Search Tree using Linked List Concept which includes Traversing the tree in Inorder, Preorder and Postorder Methods and also searching the element in the Tree
Introduction of Data Structures and Algorithms by GOWRU BHARATH KUMARBHARATH KUMAR
Basic Terminology: Elementary Data Organization:
Data Structures Usage
Data Structures Implementation
CLASSIFICATION OF DATA STRUCTURES
DATA STRUCTURES OPERATIONS
Space-Time Trade-off
Searching Algorithms
In computer science, a data structure is a data organization, management, and storage format that enables efficient access and modification. More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data. https://apkleet.com
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introduction to binary tree , binary search tree, operations of binary search tree, insertion, deletions, search. 3 cases of deletions, graph tree of binary search tree, complaxcity, notations,algorithms, Data Analysis
a. Concept and Definition
b. Binary Tree
c. Introduction and application
d. Operation
e. Types of Binary Tree
• Complete
• Strictly
• Almost Complete
f. Huffman algorithm
g. Binary Search Tree
• Insertion
• Deletion
• Searching
h. Tree Traversal
• Pre-order traversal
• In-order traversal
• Post-order traversal
Slides at myblog
http://www.ashimlamichhane.com.np/2016/07/tree-slide-for-data-structure-and-algorithm/
Assignments at github
https://github.com/ashim888/dataStructureAndAlgorithm/tree/dev/Assignments/assignment_7
Presentation On Binary Search Tree using Linked List Concept which includes Traversing the tree in Inorder, Preorder and Postorder Methods and also searching the element in the Tree
Introduction of Data Structures and Algorithms by GOWRU BHARATH KUMARBHARATH KUMAR
Basic Terminology: Elementary Data Organization:
Data Structures Usage
Data Structures Implementation
CLASSIFICATION OF DATA STRUCTURES
DATA STRUCTURES OPERATIONS
Space-Time Trade-off
Searching Algorithms
Static arrays are structures whose size is fixed at compile time and.pdfanjanacottonmills
Static arrays are structures whose size is fixed at compile time and therefore cannot be extended
or reduced to fit the data set. A dynamic array can be extended by doubling the size but there is
overhead associated with the operation of copying old data and freeing the memory associated
with the old data structure. One potential problem of using arrays for storing data is that arrays
require a contiguous block of memory which may not be available, if the requested contiguous
block is too large. However the advantages of using arrays are that each element in the array can
be accessed very efficiently using an index. However, for applications that can be better
managed without using contiguous memory we define a concept called “linked lists”.
A linked list is a collection of objects linked together by references from one object to another
object. By convention these objects are named as nodes. So the basic linked list is collection of
nodes where each node contains one or more data fields AND a reference to the next node. The
last node points to a NULL reference to indicate the end of the list.
Types of Linked Lists
Linked lists are widely used in many applications because of the flexibility it provides. Unlike
arrays that are dynamically assigned, linked lists do not require memory from a contiguous
block. This makes it very appealing to store data in a linked list, when the data set is large or
device (eg: PDA) has limited memory. One of the disadvantages of linked lists is that they are
not random accessed like arrays. To find information in a linked list one must start from the head
of the list and traverse the list sequentially until it finds (or not find) the node. Another advantage
of linked lists over arrays is that when a node is inserted or deleted, there is no need to “adjust”
the array.
There are few different types of linked lists. A singly linked list as described above provides
access to the list from the head node. Traversal is allowed only one way and there is no going
back. A doubly linked list is a list that has two references, one to the next node and another to
previous node. Doubly linked list also starts from head node, but provide access both ways. That
is one can traverse forward or backward from any node. A multilinked list (see figures 1 & 2) is
a more general linked list with multiple links from nodes. For examples, we can define a Node
that has two references, age pointer and a name pointer. With this structure it is possible to
maintain a single list, where if we follow the name pointer we can traverse the list in alphabetical
order of names and if we traverse the age pointer, we can traverse the list sorted by ages. This
type of node organization may be useful for maintaining a customer list in a bank where same
list can be traversed in any order (name, age, or any other criteria) based on the need.
Designing the Node of a Linked List
Linked list is a collection of linked nodes. A node is a struct with at least a.
This tutorial explains about linked List concept. it contains types of linked list also. All possible graphical representations are included for better understanding.
This is a presentation on Linked Lists, one of the most important topics on Data Structures and algorithms. Anyone who is new to DSA or wants to have a theoretical understanding of the same can refer to it :D
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
3. Linked list
a linear collection of data elements, called nodes
pointing to the next node by means of pointer.
a data structure consisting of a group of nodes which
together represent a sequence.
can be used to implement several other common
abstract data types, including lists (the abstract data
type), stacks, queues.
the list elements can easily be inserted or removed
without reallocation or reorganization of the entire
structure.
3
4. Advantages
a dynamic data structure, which can grow and be
pruned, allocating and deallocating memory while the
program is running.
Insertion and deletion node operations are easily
implemented in a linked list.
Linear data structures such as stacks and queues are
easily executed with a linked list.
the list elements can easily be inserted or removed
without reallocation or reorganization of the entire
structure.
4
5. Disadvantages
They have a tendency to use more memory due to pointers
requiring extra storage space.
Nodes in a linked list must be read in order from the
beginning as linked lists are inherently
sequential access.
Nodes are stored incontiguously, greatly increasing
the time required to access individual elements within
the list.
5
6. Terms
Each record of a linked list is often called an
'element' or 'node'.
The field of each node that contains the address of
the next node is usually called the 'next link' or 'next
pointer'. The remaining fields are known as the 'data',
'information', 'value', 'cargo', or 'payload' fields.
The 'head' of a list is its first node. The 'tail' of a
list may refer either to the rest of the list after
the head, or to the last node in the list.
6
head
tail
data next link
7. Singly linked list
Singly linked lists contain nodes which have a data
field as well as a 'next' field, which points to the next
node in line of nodes.
Operations that can be performed on singly linked
lists include insertion, deletion and traversal.
7
8. Doubly linked list
each node contains, besides the next-node link, a second
link field pointing to the 'previous' node in the
sequence.
The two links may be called 'forward('s') and
'backwards', or 'next' and 'prev'('previous').
Many modern operating systems use doubly linked lists
to maintain references to active processes, threads,
and other dynamic objects.
A common strategy for rootkits to evade detection is to
unlink themselves from these lists.
8
9. Multiple linked list
each node contains two or more link fields, each field
being used to connect the same set of data records in
a different order (e.g., by name, by department, by
date of birth, etc.).
9
10. Practice: Tree
Implement a node for a tree data structure.
a node can have zero or more child nodes.
10
11. Root – The top node in a tree.
Child – A node directly connected to another node when
moving away from the Root.
Parent – The converse notion of a child.
Siblings – Nodes with the same parent.
Descendant – A node reachable by repeated proceeding
from parent to child.
Ancestor – A node reachable by repeated proceeding from
child to parent.
Leaf – A node with no children.
Internal node – A node with at least one child
External node – A node with no children.
11
12. Degree – Number of sub trees of a node.
Edge – Connection between one node to another.
Path – A sequence of nodes and edges connecting a node
with a descendant.
Level – The level of a node is defined by 1 + (the
number of connections between the node and the root).
Height of node – The height of a node is the number of
edges on the longest downward path between that node
and a leaf.
Height of tree – The height of a tree is the height of
its root node.
12
13. when node '6' is concerned;
the degree is 2, number of child nodes.
the path from root is '2''7''6'
node '6' is at level 3.
13
child
sibling
parent
ancestor
descendent
15. Circular Linked list
In the last node of a list, the link field often
contains a null reference, a special value used to
indicate the lack of further nodes.
A less common convention is to make it point to the
first node of the list; in that case the list is said
to be 'circular' or 'circularly linked'; otherwise it is
said to be 'open' or 'linear'.
15
16. Sentinel nodes
In some implementations an extra 'sentinel' or 'dummy'
node may be added before the first data record or
after the last one.
This convention simplifies and accelerates some list-
handling algorithms, by ensuring that all links can be
safely dereferenced and that every list (even one that
contains no data elements) always has a "first" and
"last" node.
16
17. List handles
Since a reference to the first node gives access to
the whole list, that reference is often called the
'address', 'pointer', or 'handle' of the list.
Algorithms that manipulate linked lists usually get
such handles to the input lists and return the handles
to the resulting lists.
In some situations, it may be convenient to refer to a
list by a handle that consists of two links, pointing
to its first and last nodes.
17
18. example: list handle
int main()
{
std::list<int> intList;
intList.assign({ 1, 3, 5 });
std::list<int>::iterator listHandle = intList.begin();
listHandle++; // listHandle indicates node '3'
intList.insert(listHandle, 9); // 1, 9, 3, 5 and listHandle indicates '3'
consistently
intList.insert(listHandle, 99); // 1, 9, 99, 3, 5
for (int c : intList) {
std::cout << c << 'n';
}
return 0;
}
18
19. example
int main()
{
std::list<int> intList;
intList.assign({ 1, 3, 5 });
std::list<int>::iterator listHandle = intList.begin();
listHandle++; // listHandle indicates node '3'
intList.insert(listHandle, 9); // 1, 9, 3, 5 and listHandle indicates '3'
consistently
intList.insert(listHandle, 99); // 1, 9, 99, 3, 5
for (int c : intList) {
std::cout << c << 'n';
}
return 0;
}
19
20. example
int main()
{
std::list<int> intList;
intList.assign({ 1, 3, 5 });
std::list<int>::iterator listHandle = intList.begin();
listHandle++; // listHandle indicates node '3'
intList.insert(listHandle, 9); // 1, 9, 3, 5 and listHandle indicates '3'
consistently
intList.insert(listHandle, 99); // 1, 9, 99, 3, 5
for (int c : intList) {
std::cout << c << 'n';
}
return 0;
}
20
21. Singly linked list
struct KNode
{
int data;
KNode* next;
};
InsertAfter( KNode* node, KNode* newNode);
21
23. Practice: simple linked list
implement a KLinkedList which uses KNode.
KLinkedList must support below methods:
– InsertAfter()
– RemoveAfter()
23
25. logarithm.
In mathematics, the logarithm is the inverse operation to
exponentiation.
That means the logarithm of a number is the exponent to
which another fixed value, the base, must be raised to
produce that number.
In simple cases the logarithm counts repeated
multiplication.
For example, the base 10 logarithm of 1000 is 3, as 10 to
the power 3 is 1000 (1000 = 10×10×10 = 10 3
); the
multiplication is repeated three times.
25
26. The logarithm
of x to base b, denoted
logb(x), is the unique
real number y such that
by
= x.
For example, as 64 =
26
, we have log 2(64) = 6.
The logarithm to
base 10 (that is b = 10) is
called the
common logarithm and has
many applications in
science and engineering.
26
27. A full 3-ary tree can be used to visualize the
exponents of 3 and how the logarithm function relates
to them.
27
28. big O notation
find node in a linked list.
– O(n)
bubble sort.
– O(n2
)
binary search.
– O(log(n))
28
29. Practice: skill inventory with timer
In morpg game, we maintains skill inventories.
When a skill is used, there is a delay time so we must
wait to reuse the skill again.
We maintains skill nodes using a linked list.
On each frame move, we must calculate expiring times
of all activated skill nodes in the skill inventory.
implement skill inventory with efficient algorithm.
– modify KNode and KLinkedList.
29
30. Binary search tree
Binary search requires that we have fast access to two
elements—specifically the median elements above and
below the given node.
To combine these ideas, we need a “linked list” with
two pointers per node.
– This is the basic idea behind binary search trees.
A rooted binary tree is recursively defined as either
being (1) empty, or (2) consisting of a node called
the root, together with two rooted binary trees called
the left and right subtrees, respectively.
30
31. A binary search tree labels each node in a binary tree
with a single key such that for any node labeled x, all
nodes in the left subtree of x have keys < x while all
nodes in the right subtree of x have keys > x.
31
32. implementing binary search trees
typedef struct tree {
item_type item; // data item
struct tree* parent; // pointer to
parent
struct tree* left; // pointer to
left child
struct tree* right; // pointer to
right child
} tree;
32
33. searching in a tree
tree *search_tree(tree *l, item_type x)
{
if (l == NULL) return(NULL);
if (l->item == x) return(l);
if (x < l->item)
return( search_tree(l->left, x) );
else
return( search_tree(l->right, x) );
}
33
34. finding minimum element in a tree
tree *find_minimum(tree *t)
{
tree *min; // pointer to minimum
if (t == NULL) return(NULL);
min = t;
while (min->left != NULL)
min = min->left;
return(min);
}
34
35. traversing in a tree
void traverse_tree(tree *l)
{
if (l != NULL) {
traverse_tree(l->left);
process_item(l->item);
traverse_tree(l->right);
}
}
35
36. insertion in a tree
insert_tree(tree **l, item_type x, tree *parent)
{
tree *p; /* temporary pointer */
if (*l == NULL) {
p = malloc(sizeof(tree)); /* allocate new node */
p->item = x;
p->left = p->right = NULL;
p->parent = parent;
*l = p; /* link into parent’s record */
return;
}
if (x < (*l)->item)
insert_tree(&((*l)->left), x, *l);
else
insert_tree(&((*l)->right), x, *l);
}
36
38. How good are binary search trees?
Unfortunately, bad things can happen when building
trees through insertion.
The data structure has no control over the order of
insertion. Consider what happens if the user inserts
the keys in sorted order. The operations insert(a),
followed by insert(b), insert(c), insert(d), . . .
will produce a skinny linear height tree where only
right pointers are used.
38
39. B-tree
In computer science, a B-tree is a self-balancing tree
data structure that keeps data sorted and allows
searches, sequential access, insertions, and deletions
in logarithmic time.
The B-tree is a generalization of a binary search
tree in that a node can have more than two children.
39
40. In B-trees, internal (non-leaf) nodes can have a
variable number of child nodes within some pre-defined
range. When data is inserted or removed from a node,
its number of child nodes changes. In order to
maintain the pre-defined range, internal nodes may be
joined or split.
Each internal node of a B-tree will contain a number
of keys. The keys act as separation values which divide
its subtrees.
For example, if an internal node has 3 child nodes (or
subtrees) then it must have 2 keys: a1 and a2. All values
in the leftmost subtree will be less than a1, all
values in the middle subtree will be between a1 and a2,
and all values in the rightmost subtree will be
greater than a2.40
41. Insertion
If the node contains fewer than the maximum legal
number of elements, then there is room for the new
element. Insert the new element in the node, keeping
the node's elements ordered.
Otherwise the node is full, evenly split it into two
nodes so:
– A single median is chosen from among the leaf's elements and
the new element.
– Values less than the median are put in the new left node and
values greater than the median are put in the new right node,
with the median acting as a separation value.
– The separation value is inserted in the node's parent, which may
cause it to be split, and so on(rule A). If the node has no
parent (i.e., the node was the root), create a new root above
this node (increasing the height of the tree)(rule B).
41
42. 42
rule B applied for '2'
only rule A applied
rule B applied for '6'
43. Initial construction
For example, if the leaf nodes have maximum size 4 and
the initial collection is the integers 1 through 24,
we would initially construct 4 leaf nodes containing 5
values each and 1 which contains 4 values:
suppose the internal nodes contain at most 2 values (3
child pointers).
43
44. We build the next level up from the leaves by taking
the last element from each leaf node except the last
one.
Again, each node except the last will contain one
extra value. In the example, suppose the internal
nodes contain at most 2 values (3 child pointers).
44
45. This process is continued until we reach a level with
only one node and it is not overfilled.
45
the list elements can easily be inserted or removed without reallocation or reorganization of the entire structure because the data items need not be stored contiguously in memory.
The order among “brother” nodes matters in rooted trees, so left is different from right.
Figure 3.2 gives the shapes of the five distinct binary trees that can be formed on three nodes.
This search tree labeling scheme is very special. For any binary tree on n nodes, and any set of n keys, there is exactly one labeling that makes it a binary search tree. The allowable labelings for three-node trees are given in Figure 3.2.
There are three cases, illustrated in Figure 3.4. Leaf nodes have no children, and
so may be deleted by simply clearing the pointer to the given node.
The case of the doomed node having one child is also straightforward. There
is one parent and one grandchild, and we can link the grandchild directly to the
parent without violating the in-order labeling property of the tree.
But what of a to-be-deleted node with two children? Our solution is to relabel
this node with the key of its immediate successor in sorted order. This successor
must be the smallest value in the right subtree, specifically the leftmost descendant
in the right subtree (p). Moving this to the point of deletion results in a properlylabeled
binary search tree, and reduces our deletion problem to physically removing
a node with at most one child—a case that has been resolved above.
The full implementation has been omitted here because it looks a little ghastly,
but the code follows logically from the description above.
The worst-case complexity analysis is as follows. Every deletion requires the
cost of at most two search operations, each taking O(h) time where h is the height
of the tree, plus a constant amount of pointer manipulation.
Unlike self-balancing binary search trees, the B-tree is optimized for systems that read and write large blocks of data. B-trees are a good example of a data structure for external memory. It is commonly used in databases and filesystems.