This document provides an overview of the C programming language. It discusses that C was developed in 1972 and its advantages include being simple, reliable, and easy to use. It outlines the steps to learning C, including understanding the character set, constants like integers and characters, variables, and keywords. The document provides examples of integer and character constants and rules for constructing them. It also defines variables and lists rules for constructing them. Finally, it provides a simple "Hello World" example C program to demonstrate basic syntax like functions, arithmetic operators, and output statements.
OVERVIEW:
Introduction
Definition
Example of Threaded BT.
Types & Structure
One-way .
Double-way.
Structure.
Traversal
Algorithm for Traversal
Traversal Example
Inserting
Algorithm for Inserting
Inserting Example
Comparison With Binary Tree
Advantages and Disadvantages
Why Threaded BT are used?
Conclusion
Reference
This document discusses if-else if-else conditional statements in C programming. It begins with an introduction to C and its program structure. It then explains header files, the main function, and conditional statements. The rest of the document provides examples of using if statements, if-else statements, and if-else if statements to execute code conditionally based on different boolean expressions. It concludes by referencing additional online resources for learning about conditional statements in C.
This document contains a test for a computer science course. It has two parts: Part A contains short answer questions about parsing and grammars. Part B contains longer problems where students must apply parsing concepts. The test covers topics like LR parsing, SLR parsing, LL parsing, shift-reduce parsing, operator precedence parsing, and grammar concepts like non-terminals, terminals, left recursion, and FIRST/FOLLOW sets. Students must answer all of Part A and three of five problems in Part B.
[C++ korea] effective modern c++ study item 4 - 6 신촌Seok-joon Yun
The document discusses effective study of modern C++. It mentions using Visual Studio and Xcode compilers to study C++ and see error messages. It notes that C++ type deduction rules are essential to understand as tools may not always provide helpful or accurate results. The document encourages continued learning of C++ concepts.
This document summarizes key differences between C and C++. It discusses how C++ made all data types first-class objects, introduced classes with data hiding and member functions, allowed more flexible variable declarations, and added new features like bool, enum types and comments. C++ aimed to make C more object-oriented and added object-oriented programming concepts.
Dokumen tersebut membahas tentang algoritma Eclat dalam menemukan pola asosiasi pada basis data. Algoritma Eclat digunakan untuk mengatasi kelemahan algoritma Apriori yang membutuhkan waktu yang lama karena scanning database berulang kali. Algoritma Eclat bekerja dengan mengkompresi data kedalam struktur pohon FP-Tree untuk menghindari pengulangan scanning database. Frequent itemset diekstrak langsung dari FP-Tree menggunakan prinsip divide
BioWeka is an extension of the Weka data mining framework for bioinformatics applications. It provides additional tools for tasks like sequence analysis, gene expression analysis, and protein structure prediction. BioWeka implements these tools as extendable components within Weka's framework using common data formats and interfaces in order to improve interoperability and allow easy comparison of different methods. It has been used in applications like predicting the coding frame of sequences and distinguishing plant and pathogen genes.
This document provides an overview of the C programming language. It discusses that C was developed in 1972 and its advantages include being simple, reliable, and easy to use. It outlines the steps to learning C, including understanding the character set, constants like integers and characters, variables, and keywords. The document provides examples of integer and character constants and rules for constructing them. It also defines variables and lists rules for constructing them. Finally, it provides a simple "Hello World" example C program to demonstrate basic syntax like functions, arithmetic operators, and output statements.
OVERVIEW:
Introduction
Definition
Example of Threaded BT.
Types & Structure
One-way .
Double-way.
Structure.
Traversal
Algorithm for Traversal
Traversal Example
Inserting
Algorithm for Inserting
Inserting Example
Comparison With Binary Tree
Advantages and Disadvantages
Why Threaded BT are used?
Conclusion
Reference
This document discusses if-else if-else conditional statements in C programming. It begins with an introduction to C and its program structure. It then explains header files, the main function, and conditional statements. The rest of the document provides examples of using if statements, if-else statements, and if-else if statements to execute code conditionally based on different boolean expressions. It concludes by referencing additional online resources for learning about conditional statements in C.
This document contains a test for a computer science course. It has two parts: Part A contains short answer questions about parsing and grammars. Part B contains longer problems where students must apply parsing concepts. The test covers topics like LR parsing, SLR parsing, LL parsing, shift-reduce parsing, operator precedence parsing, and grammar concepts like non-terminals, terminals, left recursion, and FIRST/FOLLOW sets. Students must answer all of Part A and three of five problems in Part B.
[C++ korea] effective modern c++ study item 4 - 6 신촌Seok-joon Yun
The document discusses effective study of modern C++. It mentions using Visual Studio and Xcode compilers to study C++ and see error messages. It notes that C++ type deduction rules are essential to understand as tools may not always provide helpful or accurate results. The document encourages continued learning of C++ concepts.
This document summarizes key differences between C and C++. It discusses how C++ made all data types first-class objects, introduced classes with data hiding and member functions, allowed more flexible variable declarations, and added new features like bool, enum types and comments. C++ aimed to make C more object-oriented and added object-oriented programming concepts.
Dokumen tersebut membahas tentang algoritma Eclat dalam menemukan pola asosiasi pada basis data. Algoritma Eclat digunakan untuk mengatasi kelemahan algoritma Apriori yang membutuhkan waktu yang lama karena scanning database berulang kali. Algoritma Eclat bekerja dengan mengkompresi data kedalam struktur pohon FP-Tree untuk menghindari pengulangan scanning database. Frequent itemset diekstrak langsung dari FP-Tree menggunakan prinsip divide
BioWeka is an extension of the Weka data mining framework for bioinformatics applications. It provides additional tools for tasks like sequence analysis, gene expression analysis, and protein structure prediction. BioWeka implements these tools as extendable components within Weka's framework using common data formats and interfaces in order to improve interoperability and allow easy comparison of different methods. It has been used in applications like predicting the coding frame of sequences and distinguishing plant and pathogen genes.
Eclat algorithm in association rule miningDeepa Jeya
The document discusses the ECLAT algorithm for mining frequent itemsets from transactional data. ECLAT uses an equivalence class clustering approach and bottom-up lattice traversal to efficiently generate frequent itemsets in a depth-first search manner by representing the transaction data in a vertical format of item-tid lists. It improves upon the Apriori algorithm by avoiding multiple database scans and reducing memory usage through its depth-first search approach and representation of the conditional search space without having to remove items.
Apriori and Eclat algorithm in Association Rule MiningWan Aezwani Wab
The document summarizes lecture 4 of a data warehousing and mining course. It discusses association rule mining, which aims to find relationships between items in transaction data. It defines key concepts like frequent itemsets, support, confidence and association rules. It also describes algorithms like Apriori and FP-Growth for efficiently mining frequent itemsets and generating association rules from transaction data.
Prediksi Tingkat Pengangguran Kota Makassar 2011-2015bryanartika
Dokumen ini membahas tentang sistem basis data prediksi tingkat pengangguran di Kota Makassar menggunakan pola asosiasi dan aturan asosiasi. Dokumen ini membahas tentang algoritma Eclat dan FP-Growth untuk menemukan pola frekuensi itemset dan membangun pohon desision untuk memprediksi tingkat pengangguran.
This document discusses decision trees and the ID3 algorithm for generating decision trees. It explains that a decision tree classifies examples based on their attributes through a series of questions or rules. The ID3 algorithm uses information gain to choose the most informative attributes to split on at each node, resulting in a tree that maximizes classification accuracy. Some drawbacks of decision trees are that they can only handle nominal attributes and may not be robust to noisy data.
The ID3 algorithm generates a decision tree from training data using a top-down, greedy search. It calculates the entropy of attributes in the training data to determine which attribute best splits the data into pure subsets with maximum information gain. It then recursively builds the decision tree, using the selected attributes to split the data at each node until reaching leaf nodes containing only one class. The resulting decision tree can then classify new samples not in the training data.
An optimal and progressive algorithm for skyline queries slideWooSung Choi
The document presents an optimal and progressive algorithm for processing skyline queries using an R-tree index. It discusses two strategies - recursive nearest neighbor queries and a branch and bound skyline algorithm. The recursive NN query approach requires additional processing to eliminate duplicate results for higher dimensions, while the branch and bound skyline algorithm prunes non-skyline points during traversal to directly generate the skyline without duplicates. The algorithm processes the R-tree in a best-first manner by maintaining a priority queue of tree nodes ordered by their minimum possible skyline size.
The document discusses binary trees and various operations on them. It defines what a binary tree is composed of (nodes with values and pointers to left and right children). It describes tree traversals like preorder, inorder and postorder that output the nodes in different orders. It explains two common search strategies - depth-first search (DFS) and breadth-first search (BFS) - and provides examples of how they traverse a sample tree. It also briefly discusses operations like finding the minimum/maximum element, inserting a new element, and deleting an existing element from the binary search tree.
The document discusses register allocation techniques used by compilers to optimize code generation. It describes how register allocation works by constructing a register interference graph and using graph coloring algorithms to assign temporaries to a limited number of machine registers. When graph coloring fails to find a solution, spilling of temporaries is used to reduce interferences and allow coloring. Cache optimization is also briefly covered.
The document describes Karnaugh maps (K-maps), a technique for minimizing Boolean logic expressions. K-maps provide an alternate representation of truth tables where adjacent squares have a distance of 1. Logical minimization involves grouping adjacent 1s in the K-map without including any 0s to find essential prime implicants, then expressing these groups as product terms summed to obtain a minimum sum-of-products expression. An example minimizes a 2-variable function from 8 gates to 4 gates using a K-map. Don't care conditions can also be utilized for further minimization. The document concludes with a design example of a hexadecimal to 7-segment display decoder circuit minimized with K-maps.
The document contains questions and answers related to binary search trees and graphs. It discusses finding the minimum element in a binary search tree, different tree traversals like preorder, inorder and postorder, properties of binary search trees like the increasing order of inorder traversal and complexity, balance factors of binary trees, cut vertices in graphs, and properties of complete graphs.
A presentation on prim's and kruskal's algorithmGaurav Kolekar
This slides are for a presentation on Prim's and Kruskal's algorithm. Where I have tried to explain how both the algorithms work, their similarities and their differences.
We will discuss the following: Graph, Directed vs Undirected Graph, Acyclic vs Cyclic Graph, Backedge, Search vs Traversal, Breadth First Traversal, Depth First Traversal, Detect Cycle in a Directed Graph.
A threaded binary tree is a binary search tree where each node uses its left and right child pointers to either link to the in-order predecessor and successor nodes or act as threads to traverse the tree without using recursion. The document discusses how to represent threaded binary trees using additional thread fields in each node and compares them to standard binary search trees in terms of memory usage and traversal efficiency. Various applications of threaded binary trees are also presented, such as expression trees, game trees, and heap sort trees.
Reversible logic gates are an important area of research for low power circuit design. They allow information, such as inputs, to be recovered from outputs, avoiding the loss of information and heat generation. Several types of reversible logic gates are discussed in the document, including NOT, CNOT, Feynman, and Toffoli gates. Reversible logic gates have applications in areas like quantum computing, low power CMOS design, and cryptography due to their reduced heat dissipation compared to conventional logic gates. Further research on reversible logic gates could help realize more complex and systematic reversible circuits.
The document discusses algorithms for finding minimum spanning trees in graphs. It describes Kruskal's and Prim's algorithms. Kruskal's algorithm works by sorting the edges by weight and building the spanning tree by adding the shortest edges that do not create cycles. Prim's algorithm works by growing a spanning tree from an initial node by repeatedly adding the shortest edge connecting an already included node to an unincluded node. The document provides pseudocode and a walkthrough example of Kruskal's algorithm and Prim's algorithm.
The document provides an overview of sequential pattern mining. It discusses the challenges of mining sequential patterns from large databases due to the huge number of possible patterns. It then describes the Apriori algorithm as an example approach, showing the pseudocode. It works in multiple passes over the database, generating candidate itemsets in each pass and pruning those that don't meet the minimum support threshold. The document also summarizes the FP-Growth algorithm, which avoids candidate generation by building a compact FP-tree structure and mining it recursively to extract patterns. Applications mentioned include customer shopping sequences, medical treatments, and DNA sequences.
VP8 is an open source video codec developed by On2 Technologies and acquired by Google in 2010. It is designed for web-based video applications with a focus on low bandwidth and support for heterogeneous hardware. VP8 uses intra-frame and inter-frame prediction, 4x4 transform coding with an adaptive loop filter to reduce artifacts, and entropy coding with adaptive probability distributions. It achieves good quality at low bitrates and supports parallel processing for improved decoding performance on modern hardware.
This document covers exponents, logarithms, and their functions. It defines exponential and logarithmic expressions, reviews rules for manipulating exponents and logarithms, and examines the graphs of exponential and logarithmic functions. It provides examples of applying exponent and logarithm rules, and contains activities involving evaluating exponential and logarithmic expressions and graphing their related functions.
The document provides instructions for a GATE exam. It states that the exam is 3 hours long and contains questions worth 1 or 2 marks. It provides details on how answers should be marked on the answer sheet, notes on calculators and rough work, and information that negative marks will be given for incorrect answers. The instructions emphasize accurately filling out identification details on the answer sheet and carefully reading the entire paper.
Eclat algorithm in association rule miningDeepa Jeya
The document discusses the ECLAT algorithm for mining frequent itemsets from transactional data. ECLAT uses an equivalence class clustering approach and bottom-up lattice traversal to efficiently generate frequent itemsets in a depth-first search manner by representing the transaction data in a vertical format of item-tid lists. It improves upon the Apriori algorithm by avoiding multiple database scans and reducing memory usage through its depth-first search approach and representation of the conditional search space without having to remove items.
Apriori and Eclat algorithm in Association Rule MiningWan Aezwani Wab
The document summarizes lecture 4 of a data warehousing and mining course. It discusses association rule mining, which aims to find relationships between items in transaction data. It defines key concepts like frequent itemsets, support, confidence and association rules. It also describes algorithms like Apriori and FP-Growth for efficiently mining frequent itemsets and generating association rules from transaction data.
Prediksi Tingkat Pengangguran Kota Makassar 2011-2015bryanartika
Dokumen ini membahas tentang sistem basis data prediksi tingkat pengangguran di Kota Makassar menggunakan pola asosiasi dan aturan asosiasi. Dokumen ini membahas tentang algoritma Eclat dan FP-Growth untuk menemukan pola frekuensi itemset dan membangun pohon desision untuk memprediksi tingkat pengangguran.
This document discusses decision trees and the ID3 algorithm for generating decision trees. It explains that a decision tree classifies examples based on their attributes through a series of questions or rules. The ID3 algorithm uses information gain to choose the most informative attributes to split on at each node, resulting in a tree that maximizes classification accuracy. Some drawbacks of decision trees are that they can only handle nominal attributes and may not be robust to noisy data.
The ID3 algorithm generates a decision tree from training data using a top-down, greedy search. It calculates the entropy of attributes in the training data to determine which attribute best splits the data into pure subsets with maximum information gain. It then recursively builds the decision tree, using the selected attributes to split the data at each node until reaching leaf nodes containing only one class. The resulting decision tree can then classify new samples not in the training data.
An optimal and progressive algorithm for skyline queries slideWooSung Choi
The document presents an optimal and progressive algorithm for processing skyline queries using an R-tree index. It discusses two strategies - recursive nearest neighbor queries and a branch and bound skyline algorithm. The recursive NN query approach requires additional processing to eliminate duplicate results for higher dimensions, while the branch and bound skyline algorithm prunes non-skyline points during traversal to directly generate the skyline without duplicates. The algorithm processes the R-tree in a best-first manner by maintaining a priority queue of tree nodes ordered by their minimum possible skyline size.
The document discusses binary trees and various operations on them. It defines what a binary tree is composed of (nodes with values and pointers to left and right children). It describes tree traversals like preorder, inorder and postorder that output the nodes in different orders. It explains two common search strategies - depth-first search (DFS) and breadth-first search (BFS) - and provides examples of how they traverse a sample tree. It also briefly discusses operations like finding the minimum/maximum element, inserting a new element, and deleting an existing element from the binary search tree.
The document discusses register allocation techniques used by compilers to optimize code generation. It describes how register allocation works by constructing a register interference graph and using graph coloring algorithms to assign temporaries to a limited number of machine registers. When graph coloring fails to find a solution, spilling of temporaries is used to reduce interferences and allow coloring. Cache optimization is also briefly covered.
The document describes Karnaugh maps (K-maps), a technique for minimizing Boolean logic expressions. K-maps provide an alternate representation of truth tables where adjacent squares have a distance of 1. Logical minimization involves grouping adjacent 1s in the K-map without including any 0s to find essential prime implicants, then expressing these groups as product terms summed to obtain a minimum sum-of-products expression. An example minimizes a 2-variable function from 8 gates to 4 gates using a K-map. Don't care conditions can also be utilized for further minimization. The document concludes with a design example of a hexadecimal to 7-segment display decoder circuit minimized with K-maps.
The document contains questions and answers related to binary search trees and graphs. It discusses finding the minimum element in a binary search tree, different tree traversals like preorder, inorder and postorder, properties of binary search trees like the increasing order of inorder traversal and complexity, balance factors of binary trees, cut vertices in graphs, and properties of complete graphs.
A presentation on prim's and kruskal's algorithmGaurav Kolekar
This slides are for a presentation on Prim's and Kruskal's algorithm. Where I have tried to explain how both the algorithms work, their similarities and their differences.
We will discuss the following: Graph, Directed vs Undirected Graph, Acyclic vs Cyclic Graph, Backedge, Search vs Traversal, Breadth First Traversal, Depth First Traversal, Detect Cycle in a Directed Graph.
A threaded binary tree is a binary search tree where each node uses its left and right child pointers to either link to the in-order predecessor and successor nodes or act as threads to traverse the tree without using recursion. The document discusses how to represent threaded binary trees using additional thread fields in each node and compares them to standard binary search trees in terms of memory usage and traversal efficiency. Various applications of threaded binary trees are also presented, such as expression trees, game trees, and heap sort trees.
Reversible logic gates are an important area of research for low power circuit design. They allow information, such as inputs, to be recovered from outputs, avoiding the loss of information and heat generation. Several types of reversible logic gates are discussed in the document, including NOT, CNOT, Feynman, and Toffoli gates. Reversible logic gates have applications in areas like quantum computing, low power CMOS design, and cryptography due to their reduced heat dissipation compared to conventional logic gates. Further research on reversible logic gates could help realize more complex and systematic reversible circuits.
The document discusses algorithms for finding minimum spanning trees in graphs. It describes Kruskal's and Prim's algorithms. Kruskal's algorithm works by sorting the edges by weight and building the spanning tree by adding the shortest edges that do not create cycles. Prim's algorithm works by growing a spanning tree from an initial node by repeatedly adding the shortest edge connecting an already included node to an unincluded node. The document provides pseudocode and a walkthrough example of Kruskal's algorithm and Prim's algorithm.
The document provides an overview of sequential pattern mining. It discusses the challenges of mining sequential patterns from large databases due to the huge number of possible patterns. It then describes the Apriori algorithm as an example approach, showing the pseudocode. It works in multiple passes over the database, generating candidate itemsets in each pass and pruning those that don't meet the minimum support threshold. The document also summarizes the FP-Growth algorithm, which avoids candidate generation by building a compact FP-tree structure and mining it recursively to extract patterns. Applications mentioned include customer shopping sequences, medical treatments, and DNA sequences.
VP8 is an open source video codec developed by On2 Technologies and acquired by Google in 2010. It is designed for web-based video applications with a focus on low bandwidth and support for heterogeneous hardware. VP8 uses intra-frame and inter-frame prediction, 4x4 transform coding with an adaptive loop filter to reduce artifacts, and entropy coding with adaptive probability distributions. It achieves good quality at low bitrates and supports parallel processing for improved decoding performance on modern hardware.
This document covers exponents, logarithms, and their functions. It defines exponential and logarithmic expressions, reviews rules for manipulating exponents and logarithms, and examines the graphs of exponential and logarithmic functions. It provides examples of applying exponent and logarithm rules, and contains activities involving evaluating exponential and logarithmic expressions and graphing their related functions.
The document provides instructions for a GATE exam. It states that the exam is 3 hours long and contains questions worth 1 or 2 marks. It provides details on how answers should be marked on the answer sheet, notes on calculators and rough work, and information that negative marks will be given for incorrect answers. The instructions emphasize accurately filling out identification details on the answer sheet and carefully reading the entire paper.
The document describes a C program to multiply two matrices. It explains that the program takes input of rows and columns for Matrix A and B, reads in the element values, and checks that the column of the first matrix equals the row of the second before calculating the product. An example is provided where the matrices can be multiplied, producing the output matrix, and another where they cannot due to mismatched dimensions. Requirements for the program include pointers, 2D arrays, and dynamic memory allocation.
An efficient map-reduce algorithm is presented for computing formal concepts from binary datasets in a single iteration. The algorithm first uses map-reduce to generate a sufficient set of concepts that can be used to enumerate the entire lattice of formal concepts. It then processes the reduced output on a single machine to generate the sufficient set. Finally, it selectively enumerates all formal concepts in the lattice by using the sufficient set, which avoids computing the entire lattice. This approach improves efficiency over previous algorithms that required multiple map-reduce iterations or sequential processing of the entire lattice.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.pptHenry Hollis
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
3. • Gambaran penyilangan Itemset Lattice
– Breadth-first(Menyeluruh) vs Depth-first(Mendalam)
(a) Breadth first (b) Depth first
Metode Pencarian Alternatif
4. ECLAT: Metode Pembentukan Itemset
• ECLAT: untuk setiap item, dinyatakan dalam tabel
transaction ids (tids); tampilan data vertikal
TID Items
1 A,B,E
2 B,C,D
3 C,E
4 A,C,D
5 A,B,C,D
6 A,E
7 A,B
8 A,B,C
9 A,C,D
10 B
Horizontal
Data Layout
A B C D E
1 1 2 2 1
4 2 3 4 3
5 5 4 5 6
6 7 8 9
7 8 9
8 10
9
Vertical Data Layout
TID-list
5. ECLAT: Metode Pembentukan Itemset
• Tentukan support (pendukung) dari setiap k-itemset dengan
menyilangkan tid-lists dari kedua (k-1) subset.
• 3 pendekatan penyilangan:
– Atas-bawah, bawah-atas dan gabungan
• Keuntungan: Proses hitung support lebih cepat dibandingkan
algoritma apriori
• Kerugian: ukuran tid (vertikal) lebih besar dibandingkan
apriori, sehingga memenuhi memori
A
1
4
5
6
7
8
9
B
1
2
5
7
8
10
AB
1
5
7
8
6. First scan – determine frequent 1-
itemsets, then build header
TID Items
1 {A,B}
2 {B,C,D}
3 {A,C,D,E}
4 {A,D,E}
5 {A,B,C}
6 {A,B,C,D}
7 {B,C}
8 {A,B,C}
9 {A,B,D}
10 {B,C,E}
B 8
A 7
C 7
D 5
E 3
7. FP-tree construction
TID Items
1 {A,B}
2 {B,C,D}
3 {A,C,D,E}
4 {A,D,E}
5 {A,B,C}
6 {A,B,C,D}
7 {B,C}
8 {A,B,C}
9 {A,B,D}
10 {B,C,E}
null
B:1
A:1
After reading TID=1:
After reading TID=2:
null
B:2
A:1
C:1
D:1
8. FP-Tree Construction
TID Items
1 {A,B}
2 {B,C,D}
3 {A,C,D,E}
4 {A,D,E}
5 {A,B,C}
6 {A,B,C,D}
7 {B,C}
8 {A,B,C}
9 {A,B,D}
10 {B,C,E}
Transaction
Database
Item Pointer
B 8
A 7
C 7
D 5
E 3
Header table
B:8
A:5
null
C:3
D:1
A:2
C:1
D:1
E:1
D:1
E:1C:3
D:1
D:1 E:1
Chain pointers help in quickly finding all the paths
of the tree containing some given item.
9. FP-Growth (I)
• FP-growth generates frequent itemsets from an FP-tree by
exploring the tree in a bottom-up fashion.
• Given the example tree, the algorithm looks for frequent
itemsets ending in E first, followed by D, C, A, and finally, B.
• Since every transaction is mapped onto a path in the FP-tree, we
can derive the frequent itemsets ending with a particular item,
say, E, by examining only the paths containing node E.
• These paths can be accessed rapidly using the pointers
associated with node E.
11. Conditional FP-Tree for E
• We now need to build a conditional FP-Tree for E, which is the
tree of itemsets include in E.
• It is not the tree obtained in previous slide as result of deleting
nodes from the original tree.
• Why? Because the order of the items change.
– In this example, D has a higher than E count.
12. Conditional FP-Tree for E
Adding up the counts for D we get
2, so {E,D} is frequent itemset.
We continue recursively.
Base of recursion: When the tree
has a single path only.
B:3
null
C:3
A:2
C:1
D:1
E:1
D:1
E:1E:1
The set of paths containing E.
Insert each path (after truncating
E) into a new tree.
Item Pointer
C 4
B 3
A 2
D 2
Header table
The new
header
C:3
null
B:3
C:1
A:1
D:1
A:1
D:1
The
conditional
FP-Tree for E
13. FP-Tree Another Example
A B C E F O
A C G
E I
A C D E G
A C E G L
E J
A B C E F P
A C D
A C E G M
A C E G N
A:8
C:8
E:8
G:5
B:2
D:2
F:2
A C E B F
A C G
E
A C E G D
A C E G
E
A C E B F
A C D
A C E G
A C E G
Freq. 1-Itemsets.
Supp. Count 2
Transactions Transactions with items sorted based
on frequencies, and ignoring the
infrequent items.
14. FP-Tree after reading 1st transaction
A:8
C:8
E:8
G:5
B:2
D:2
F:2
A C E B F
A C G
E
A C E G D
A C E G
E
A C E B F
A C D
A C E G
A C E G
null
A:1
C:1
E:1
B:1
F:1
Header
15. FP-Tree after reading 2nd transaction
A C E B F
A C G
E
A C E G D
A C E G
E
A C E B F
A C D
A C E G
A C E G
G:1
A:8
C:8
E:8
G:5
B:2
D:2
F:2
null
A:2
C:2
E:1
B:1
F:1
Header
16. FP-Tree after reading 3rd transaction
A C E B F
A C G
E
A C E G D
A C E G
E
A C E B F
A C D
A C E G
A C E G
G:1
A:8
C:8
E:8
G:5
B:2
D:2
F:2
null
A:2
C:2
E:1
B:1
F:1
Header
E:1
17. A C E B F
A C G
E
A C E G D
A C E G
E
A C E B F
A C D
A C E G
A C E G
FP-Tree after reading 4th transaction
G:1
A:8
C:8
E:8
G:5
B:2
D:2
F:2
null
A:3
C:3
E:2
B:1
F:1
Header
E:1
G:1
D:1
18. A C E B F
A C G
E
A C E G D
A C E G
E
A C E B F
A C D
A C E G
A C E G
FP-Tree after reading 5th transaction
G:1
A:8
C:8
E:8
G:5
B:2
D:2
F:2
null
A:4
C:4
E:3
B:1
F:1
Header
E:1
G:2
D:1
19. A C E B F
A C G
E
A C E G D
A C E G
E
A C E B F
A C D
A C E G
A C E G
FP-Tree after reading 6th transaction
G:1
A:8
C:8
E:8
G:5
B:2
D:2
F:2
null
A:4
C:4
E:3
B:1
F:1
Header
E:2
G:2
D:1
20. A C E B F
A C G
E
A C E G D
A C E G
E
A C E B F
A C D
A C E G
A C E G
FP-Tree after reading 7th transaction
G:1
A:8
C:8
E:8
G:5
B:2
D:2
F:2
null
A:5
C:5
E:4
B:2
F:2
Header
E:2
G:2
D:1
21. A C E B F
A C G
E
A C E G D
A C E G
E
A C E B F
A C D
A C E G
A C E G
FP-Tree after reading 8th transaction
G:1
A:8
C:8
E:8
G:5
B:2
D:2
F:2
null
A:6
C:6
E:4
B:2
F:2
Header
E:2
G:2
D:1
D:1
22. A C E B F
A C G
E
A C E G D
A C E G
E
A C E B F
A C D
A C E G
A C E G
FP-Tree after reading 9th transaction
G:1
A:8
C:8
E:8
G:5
B:2
D:2
F:2
null
A:7
C:7
E:5
B:2
F:2
Header
E:2
G:3
D:1
D:1
23. A C E B F
A C G
E
A C E G D
A C E G
E
A C E B F
A C D
A C E G
A C E G
FP-Tree after reading 10th transaction
G:1
A:8
C:8
E:8
G:5
B:2
D:2
F:2
null
A:8
C:8
E:6
B:2
F:2
Header
E:2
G:4
D:1
D:1
24. Conditional FP-Tree for F
A:8
C:8
E:8
G:5
B:2
D:2
F:2
null
A:8
C:8
E:6
B:2
F:2
Header
There is only a single path containing F
A:2
C:2
E:2
B:2
null
A:2
C:2
E:2
B:2
New Header
25. Recursion
• We continue recursively on the
conditional FP-Tree for F.
• However, when the tree is just a
single path it is the base case for
the recursion.
• So, we just produce all the subsets
of the items on this path merged
with F.
{F} {A,F} {C,F} {E,F} {B,F}
{A,C,F}, …,
{A,C,E,F}
A:6
C:6
E:5
B:2
null
A:2
C:2
E:2
B:2
New Header
26. Conditional FP-Tree for D
A:2
C:2
null
A:2
C:2
New Headernull
A:8
C:8
E:6
G:4
D:1
D:1
Paths containing D after updating the counts
The other items are
removed as infrequent.
The tree is just a single path; it is
the base case for the recursion.
So, we just produce all the
subsets of the items on this path
merged with D.
{D} {A,D} {C,D} {A,C,D}