The document discusses the Apriori algorithm for finding frequent itemsets in transactional data. The Apriori algorithm works in two phases:
1. It finds all frequent itemsets of length 1 by scanning the transaction database. It then generates candidate itemsets of length k from frequent itemsets of length k-1, and tests them against the database to determine which are frequent.
2. It uses the found frequent itemsets to generate association rules between items. The confidence and support of each rule is calculated to determine how interesting it is.
The algorithm efficiently finds all rules and itemsets that meet a minimum support threshold by generating candidates in a way that prunes subsets that cannot be frequent. This allows
Lecture 4: Frequent Itemests, Association Rules. Evaluation. Beyond Apriori (ppt, pdf)
Chapter 6 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar.
Chapter 6 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman.
This document discusses association rule mining. It begins by defining the task of association rule mining as finding rules that predict the occurrence of items based on other items in transactions. It then describes how association rules are generated in two steps: first finding frequent itemsets whose support is above a minimum threshold, and then generating rules from those itemsets. The key challenge is that a brute force approach is computationally prohibitive due to the huge number of possible rules, so techniques like the Apriori algorithm are used to prune the search space.
This chapter discusses frequent pattern mining, which involves finding patterns that frequently occur in transactional or other forms of data. It covers basic concepts like frequent itemsets and association rules. It also describes several algorithms for efficiently mining frequent patterns at scale, including Apriori, FP-Growth, and the ECLAT algorithm. These algorithms aim to address the computational challenges of candidate generation and database scanning.
The comparative study of apriori and FP-growth algorithmdeepti92pawar
This document summarizes a seminar presentation comparing the Apriori and FP-Growth algorithms for association rule mining. The document introduces association rule mining and frequent itemset mining. It then describes the Apriori algorithm, including its generate-and-test approach and bottlenecks. Next, it explains the FP-Growth algorithm, including how it builds an FP-tree to efficiently extract frequent itemsets without candidate generation. Finally, it provides results comparing the performance of the two algorithms and concludes that FP-Growth is more efficient for mining long patterns.
This document discusses frequent pattern mining and association rule mining. It begins by defining frequent patterns as patterns that appear frequently in a dataset, such as frequently purchased itemsets. It then describes the Apriori algorithm for finding frequent itemsets, which uses multiple passes over the data and candidate generation. The document also introduces FP-Growth, an alternative algorithm that avoids candidate generation by compressing the database into a frequent-pattern tree. Finally, it discusses generating association rules from frequent itemsets and techniques for improving the efficiency of frequent pattern mining.
This document proposes modifications to the Apriori algorithm for association rule mining. It begins with an introduction to association rule learning and the Apriori algorithm. It then describes the proposed modifications which include:
1. Adding a "tag" field to transactions to reduce the search space when finding frequent itemsets.
2. A modified approach to generating association rules that aims to produce fewer rules while maximizing correct classification of data.
An example is provided to illustrate how the tag-based search works. The proposed modifications are intended to improve the efficiency and effectiveness of the association rule mining process. The document concludes by discussing experimental results comparing the proposed approach to other rule learning algorithms on an iris dataset.
MODULE 5 _ Mining frequent patterns and associations.pptxnikshaikh786
1. The FP-Growth algorithm constructs an FP-tree to store transaction data, with frequent items listed in descending order of frequency.
2. It then uses a divide-and-conquer strategy to mine the conditional pattern base of each frequent item prefix, extracting combinations of frequent items.
3. This recursively mines the frequent patterns from the conditional FP-tree for each prefix path, without generating a large number of candidate itemsets.
The document provides an overview of data mining concepts including association rules, classification, clustering, and applications of data mining. It discusses association rule mining algorithms like Apriori and FP-growth, decision tree algorithms for classification, and k-means clustering. It also lists some commercial data mining tools and potential applications in various domains like marketing, risk analysis, manufacturing, and bioinformatics.
Lecture 4: Frequent Itemests, Association Rules. Evaluation. Beyond Apriori (ppt, pdf)
Chapter 6 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar.
Chapter 6 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman.
This document discusses association rule mining. It begins by defining the task of association rule mining as finding rules that predict the occurrence of items based on other items in transactions. It then describes how association rules are generated in two steps: first finding frequent itemsets whose support is above a minimum threshold, and then generating rules from those itemsets. The key challenge is that a brute force approach is computationally prohibitive due to the huge number of possible rules, so techniques like the Apriori algorithm are used to prune the search space.
This chapter discusses frequent pattern mining, which involves finding patterns that frequently occur in transactional or other forms of data. It covers basic concepts like frequent itemsets and association rules. It also describes several algorithms for efficiently mining frequent patterns at scale, including Apriori, FP-Growth, and the ECLAT algorithm. These algorithms aim to address the computational challenges of candidate generation and database scanning.
The comparative study of apriori and FP-growth algorithmdeepti92pawar
This document summarizes a seminar presentation comparing the Apriori and FP-Growth algorithms for association rule mining. The document introduces association rule mining and frequent itemset mining. It then describes the Apriori algorithm, including its generate-and-test approach and bottlenecks. Next, it explains the FP-Growth algorithm, including how it builds an FP-tree to efficiently extract frequent itemsets without candidate generation. Finally, it provides results comparing the performance of the two algorithms and concludes that FP-Growth is more efficient for mining long patterns.
This document discusses frequent pattern mining and association rule mining. It begins by defining frequent patterns as patterns that appear frequently in a dataset, such as frequently purchased itemsets. It then describes the Apriori algorithm for finding frequent itemsets, which uses multiple passes over the data and candidate generation. The document also introduces FP-Growth, an alternative algorithm that avoids candidate generation by compressing the database into a frequent-pattern tree. Finally, it discusses generating association rules from frequent itemsets and techniques for improving the efficiency of frequent pattern mining.
This document proposes modifications to the Apriori algorithm for association rule mining. It begins with an introduction to association rule learning and the Apriori algorithm. It then describes the proposed modifications which include:
1. Adding a "tag" field to transactions to reduce the search space when finding frequent itemsets.
2. A modified approach to generating association rules that aims to produce fewer rules while maximizing correct classification of data.
An example is provided to illustrate how the tag-based search works. The proposed modifications are intended to improve the efficiency and effectiveness of the association rule mining process. The document concludes by discussing experimental results comparing the proposed approach to other rule learning algorithms on an iris dataset.
MODULE 5 _ Mining frequent patterns and associations.pptxnikshaikh786
1. The FP-Growth algorithm constructs an FP-tree to store transaction data, with frequent items listed in descending order of frequency.
2. It then uses a divide-and-conquer strategy to mine the conditional pattern base of each frequent item prefix, extracting combinations of frequent items.
3. This recursively mines the frequent patterns from the conditional FP-tree for each prefix path, without generating a large number of candidate itemsets.
The document provides an overview of data mining concepts including association rules, classification, clustering, and applications of data mining. It discusses association rule mining algorithms like Apriori and FP-growth, decision tree algorithms for classification, and k-means clustering. It also lists some commercial data mining tools and potential applications in various domains like marketing, risk analysis, manufacturing, and bioinformatics.
The document provides an overview of data mining concepts including association rules, classification, clustering, and applications of data mining. It discusses association rule mining algorithms like Apriori and FP-growth, decision tree algorithms for classification, and K-means clustering. Commercial data mining tools from companies like Oracle, SAS, and IBM are also mentioned. The document concludes that data mining can be used to discover patterns in many types of data and the results may include association rules, sequential patterns, and classification trees.
The document provides an overview of data mining concepts including association rules, classification, and clustering algorithms. It introduces data mining and knowledge discovery processes. Association rule mining aims to find relationships between variables in large datasets using the Apriori and FP-growth algorithms. Classification algorithms build a model to predict class membership for new records based on a decision tree. Clustering algorithms group similar records together without predefined classes.
This document summarizes a research paper that proposes a method for mining association rules from geographical points of interest data. It describes experiments conducted on point of interest data from Luoyang, China. The experiments involved (1) generating transactional data by spatially clustering the points of interest and converting each cluster to a transaction, (2) applying a novel FP-Growth algorithm called FP-GCID to generate frequent itemsets from the transaction data, and (3) ranking the association rules by mean product of probabilities to identify interesting rules. The top rules showed relationships between types of points of interest that should be considered together for deployment, such as banks and entertainment being related to catering establishments.
Lecture 3: Frequent Itemsets, Association Rules, Apriori algorithm.(ppt, pdf)
Chapter 6 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar.
Chapter 6 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman.
This document provides an overview of association rule mining and the Apriori algorithm. It introduces the concepts of association rules, frequent itemsets, support and confidence. The Apriori algorithm is a level-wise approach that first finds all frequent itemsets that satisfy a minimum support threshold, and then generates strong association rules from them that meet a minimum confidence threshold. The algorithm makes multiple passes over the transaction data and exploits an apriori property to prune the search space.
Association analysis is a technique used to uncover relationships between items in transactional data. It involves finding frequent itemsets whose occurrence exceeds a minimum support threshold, and then generating association rules from these itemsets that satisfy minimum confidence. The Apriori algorithm is commonly used for this task, as it leverages the Apriori property to prune the search space - if an itemset is infrequent, its supersets cannot be frequent. It performs multiple database scans to iteratively grow frequent itemsets and extract high confidence rules.
The document discusses association rule mining for market basket analysis. It describes the market basket model where items are products and baskets represent customer purchases. It defines key terms like support, frequent itemsets, and association rules. It also discusses the challenges of scale for large retail datasets and describes the A-Priori algorithm for efficiently finding frequent itemsets and generating association rules from transaction data.
The document discusses association rule mining for market basket analysis. It describes the market basket model where items are products and baskets represent customer purchases. It defines key terms like support, frequent itemsets, and association rules. It also discusses the challenges of scale for large retail datasets and describes the A-Priori algorithm for efficiently finding frequent itemsets and generating association rules from transaction data.
Mining Frequent Patterns, Association and CorrelationsJustin Cletus
This document summarizes Chapter 6 of the book "Data Mining: Concepts and Techniques" which discusses frequent pattern mining. It introduces basic concepts like frequent itemsets and association rules. It then describes several scalable algorithms for mining frequent itemsets, including Apriori, FP-Growth, and ECLAT. It also discusses optimizations to Apriori like partitioning the database and techniques to reduce the number of candidates and database scans.
This document presents the Dynamic Itemset Counting (DIC) algorithm for mining frequent itemsets from transactional data. DIC allows itemsets to be counted as soon as they are suspected of being frequent rather than waiting until the end of each pass over the data. The algorithm marks itemsets with solid or dashed boxes/circles to indicate their confirmed or suspected size. It iterates over the transaction data, incrementing counters for suspected itemsets and updating their markings if counts pass the minimum support threshold. The document discusses how DIC can be parallelized and extended to handle incremental updates to the transaction data.
This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.
The document discusses association rule mining which aims to discover relationships between items in transactional data. It defines key concepts like support, confidence and association rules. It also describes several algorithms for mining association rules like Apriori, Partition and Pincer-Search. Apriori is a level-wise, candidate generation-based approach that leverages the downward closure property. Partition divides the database to mine local frequent itemsets in parallel. Pincer-Search incorporates bidirectional search to prune candidates more efficiently.
The document summarizes research on improving the Apriori algorithm for association rule mining. It first provides background on association rule mining and the standard Apriori algorithm. It then discusses several proposed improvements to Apriori, including reducing the number of database scans, shrinking the candidate itemset size, and using techniques like pruning and hash trees. Finally, it outlines some open challenges for further optimizing association rule mining.
This document summarizes research on improving the Apriori algorithm for mining association rules from transactional databases. It first provides background on association rule mining and describes the basic Apriori algorithm. The Apriori algorithm finds frequent itemsets by multiple passes over the database but has limitations of increased search space and computational costs as the database size increases. The document then reviews research on variations of the Apriori algorithm that aim to reduce the number of database scans, shrink the candidate sets, and facilitate support counting to improve performance.
A PREFIXED-ITEMSET-BASED IMPROVEMENT FOR APRIORI ALGORITHMcsandit
Association rules is a very important part of data mining. It is used to find the interesting patterns from transaction databases. Apriori algorithm is one of the most classical algorithms
of association rules, but it has the bottleneck in efficiency. In this article, we proposed a prefixed-itemset-based data structure for candidate itemset generation, with the help of the structure we managed to improve the efficiency of the classical Apriori algorithm.
A PREFIXED-ITEMSET-BASED IMPROVEMENT FOR APRIORI ALGORITHMcscpconf
Association rules is a very important part of data mining. It is used to find the interesting patterns from transaction databases. Apriori algorithm is one of the most classical algorithms of association rules, but it has the bottleneck in efficiency. In this article, we proposed a prefixed-itemset-based data structure for candidate itemset generation, with the help of the structure we managed to improve the efficiency of the classical Apriori algorithm.
The document discusses two algorithms for hiding sensitive association rules in data mining:
1. The Increase Support of LHS First (ISLF) algorithm tries to increase the support of the left hand side of a rule to decrease its confidence below the minimum threshold. If unsuccessful, it decreases the support of the right hand side.
2. The Decrease Support of RHS First (DSRF) algorithm first tries to decrease the support of the right hand side of a rule containing sensitive items to lower the rule's confidence.
Both algorithms modify transactions in the database to alter item support counts and successfully hide association rules containing sensitive items on the right hand side below user-defined minimum support and confidence levels.
Association rule mining is a technique used to discover relationships between variables in large datasets. It identifies patterns and correlations among items. The key concepts are itemsets, support, and confidence. The Apriori algorithm and FP-Growth approach are two common algorithms used. Apriori generates candidate itemsets in multiple passes over the data, while FP-Growth avoids candidate generation by building a tree structure. The Eclat algorithm also finds frequent itemsets but uses a vertical database format and depth-first search. These algorithms are applied to market basket analysis to understand customer purchasing behaviors.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
The document provides an overview of data mining concepts including association rules, classification, clustering, and applications of data mining. It discusses association rule mining algorithms like Apriori and FP-growth, decision tree algorithms for classification, and K-means clustering. Commercial data mining tools from companies like Oracle, SAS, and IBM are also mentioned. The document concludes that data mining can be used to discover patterns in many types of data and the results may include association rules, sequential patterns, and classification trees.
The document provides an overview of data mining concepts including association rules, classification, and clustering algorithms. It introduces data mining and knowledge discovery processes. Association rule mining aims to find relationships between variables in large datasets using the Apriori and FP-growth algorithms. Classification algorithms build a model to predict class membership for new records based on a decision tree. Clustering algorithms group similar records together without predefined classes.
This document summarizes a research paper that proposes a method for mining association rules from geographical points of interest data. It describes experiments conducted on point of interest data from Luoyang, China. The experiments involved (1) generating transactional data by spatially clustering the points of interest and converting each cluster to a transaction, (2) applying a novel FP-Growth algorithm called FP-GCID to generate frequent itemsets from the transaction data, and (3) ranking the association rules by mean product of probabilities to identify interesting rules. The top rules showed relationships between types of points of interest that should be considered together for deployment, such as banks and entertainment being related to catering establishments.
Lecture 3: Frequent Itemsets, Association Rules, Apriori algorithm.(ppt, pdf)
Chapter 6 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar.
Chapter 6 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman.
This document provides an overview of association rule mining and the Apriori algorithm. It introduces the concepts of association rules, frequent itemsets, support and confidence. The Apriori algorithm is a level-wise approach that first finds all frequent itemsets that satisfy a minimum support threshold, and then generates strong association rules from them that meet a minimum confidence threshold. The algorithm makes multiple passes over the transaction data and exploits an apriori property to prune the search space.
Association analysis is a technique used to uncover relationships between items in transactional data. It involves finding frequent itemsets whose occurrence exceeds a minimum support threshold, and then generating association rules from these itemsets that satisfy minimum confidence. The Apriori algorithm is commonly used for this task, as it leverages the Apriori property to prune the search space - if an itemset is infrequent, its supersets cannot be frequent. It performs multiple database scans to iteratively grow frequent itemsets and extract high confidence rules.
The document discusses association rule mining for market basket analysis. It describes the market basket model where items are products and baskets represent customer purchases. It defines key terms like support, frequent itemsets, and association rules. It also discusses the challenges of scale for large retail datasets and describes the A-Priori algorithm for efficiently finding frequent itemsets and generating association rules from transaction data.
The document discusses association rule mining for market basket analysis. It describes the market basket model where items are products and baskets represent customer purchases. It defines key terms like support, frequent itemsets, and association rules. It also discusses the challenges of scale for large retail datasets and describes the A-Priori algorithm for efficiently finding frequent itemsets and generating association rules from transaction data.
Mining Frequent Patterns, Association and CorrelationsJustin Cletus
This document summarizes Chapter 6 of the book "Data Mining: Concepts and Techniques" which discusses frequent pattern mining. It introduces basic concepts like frequent itemsets and association rules. It then describes several scalable algorithms for mining frequent itemsets, including Apriori, FP-Growth, and ECLAT. It also discusses optimizations to Apriori like partitioning the database and techniques to reduce the number of candidates and database scans.
This document presents the Dynamic Itemset Counting (DIC) algorithm for mining frequent itemsets from transactional data. DIC allows itemsets to be counted as soon as they are suspected of being frequent rather than waiting until the end of each pass over the data. The algorithm marks itemsets with solid or dashed boxes/circles to indicate their confirmed or suspected size. It iterates over the transaction data, incrementing counters for suspected itemsets and updating their markings if counts pass the minimum support threshold. The document discusses how DIC can be parallelized and extended to handle incremental updates to the transaction data.
This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.This slide is about Data mining rules.
The document discusses association rule mining which aims to discover relationships between items in transactional data. It defines key concepts like support, confidence and association rules. It also describes several algorithms for mining association rules like Apriori, Partition and Pincer-Search. Apriori is a level-wise, candidate generation-based approach that leverages the downward closure property. Partition divides the database to mine local frequent itemsets in parallel. Pincer-Search incorporates bidirectional search to prune candidates more efficiently.
The document summarizes research on improving the Apriori algorithm for association rule mining. It first provides background on association rule mining and the standard Apriori algorithm. It then discusses several proposed improvements to Apriori, including reducing the number of database scans, shrinking the candidate itemset size, and using techniques like pruning and hash trees. Finally, it outlines some open challenges for further optimizing association rule mining.
This document summarizes research on improving the Apriori algorithm for mining association rules from transactional databases. It first provides background on association rule mining and describes the basic Apriori algorithm. The Apriori algorithm finds frequent itemsets by multiple passes over the database but has limitations of increased search space and computational costs as the database size increases. The document then reviews research on variations of the Apriori algorithm that aim to reduce the number of database scans, shrink the candidate sets, and facilitate support counting to improve performance.
A PREFIXED-ITEMSET-BASED IMPROVEMENT FOR APRIORI ALGORITHMcsandit
Association rules is a very important part of data mining. It is used to find the interesting patterns from transaction databases. Apriori algorithm is one of the most classical algorithms
of association rules, but it has the bottleneck in efficiency. In this article, we proposed a prefixed-itemset-based data structure for candidate itemset generation, with the help of the structure we managed to improve the efficiency of the classical Apriori algorithm.
A PREFIXED-ITEMSET-BASED IMPROVEMENT FOR APRIORI ALGORITHMcscpconf
Association rules is a very important part of data mining. It is used to find the interesting patterns from transaction databases. Apriori algorithm is one of the most classical algorithms of association rules, but it has the bottleneck in efficiency. In this article, we proposed a prefixed-itemset-based data structure for candidate itemset generation, with the help of the structure we managed to improve the efficiency of the classical Apriori algorithm.
The document discusses two algorithms for hiding sensitive association rules in data mining:
1. The Increase Support of LHS First (ISLF) algorithm tries to increase the support of the left hand side of a rule to decrease its confidence below the minimum threshold. If unsuccessful, it decreases the support of the right hand side.
2. The Decrease Support of RHS First (DSRF) algorithm first tries to decrease the support of the right hand side of a rule containing sensitive items to lower the rule's confidence.
Both algorithms modify transactions in the database to alter item support counts and successfully hide association rules containing sensitive items on the right hand side below user-defined minimum support and confidence levels.
Association rule mining is a technique used to discover relationships between variables in large datasets. It identifies patterns and correlations among items. The key concepts are itemsets, support, and confidence. The Apriori algorithm and FP-Growth approach are two common algorithms used. Apriori generates candidate itemsets in multiple passes over the data, while FP-Growth avoids candidate generation by building a tree structure. The Eclat algorithm also finds frequent itemsets but uses a vertical database format and depth-first search. These algorithms are applied to market basket analysis to understand customer purchasing behaviors.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
2. Reading
The main technical material (the Apriori
algorithm and its variants) in this lecture is
based on:
Fast Algorithms for Mining Association Rules, by
Rakesh Agrawal and Ramakrishan Sikant, IBM
Almaden Research Center
Google it, and you can get the pdf
4. `Basket data’
A very common type of data; often also called
transaction data.
Next slide shows example transaction database,
where each record represents a transaction
between (usually) a customer and a shop. Each
record in a supermarket’s transaction DB, for
example, corresponds to a basket of specific
items.
6. Numbers
Our example transaction DB has 20 records of
supermarket transactions, from a supermarket that
only sells 9 things
One month in a large supermarket with five stores
spread around a reasonably sized city might easily
yield a DB of 20,000,000 baskets, each containing
a set of products from a pool of around 1,000
7. Discovering Rules
A common and useful application of data mining
A `rule’ is something like this:
If a basket contains apples and cheese, then it also contains
beer
Any such rule has two associated measures:
1. confidence – when the `if’ part is true, how often is the
`then’ bit true? This is the same as accuracy.
2. coverage or support – how much of the database
contains the `if’ part?
8. Example:
What is the confidence and coverage of:
If the basket contains beer and cheese, then it
also contains honey
2/20 of the records contain both beer and cheese, so coverage is 10%
Of these 2, 1 contains honey, so confidence is 50%
10. Interesting/Useful rules
Statistically, anything that is interesting is something
that happens significantly more than you would
expect by chance.
E.g. basic statistical analysis of basket data may
show that 10% of baskets contain bread, and 4%
of baskets contain washing-up powder. I.e: if you
choose a basket at random:
– There is a probability 0.1 that it contains bread.
– There is a probability 0.04 that it contains washing-up
powder.
11. Bread and washing up powder
What is the probability of a basket containing
both bread and washing-up powder? The
laws of probability say:
If these two things are independent, chance is
0.1 * 0.04 = 0.004
That is, we would expect 0.4% of baskets to
contain both bread and washing up powder
12. Interesting means surprising
We therefore have a prior expectation that just 4 in
1,000 baskets should contain both bread and
washing up powder.
If we investigate, and discover that really it is 20 in
1,000 baskets, then we will be very surprised. It
tells us that:
– Something is going on in shoppers’ minds: bread and
washing-up powder are connected in some way.
– There may be ways to exploit this discovery … put the
powder and bread at opposite ends of the supermarket?
13. Finding surprising rules
Suppose we ask `what is the most surprising rule in this database? This
would be, presumably, a rule whose accuracy is more different from its
expected accuracy than any others. But it also has to have a suitable
level of coverage, or else it may be just a statistical blip, and/or
unexploitable.
Looking only at rules of the form:
if basket contains X and Y, then it also contains Z
… our realistic numbers tell us that there may be around 500,000,000
distinct possible rules. For each of these we need to work out its
accuracy and coverage, by trawling through a database of around
20,000,000 basket records. … c 1016 operations …
… we need more efficient ways to find such rules
14. There is nothing very special or clever about Apriori; but
it is simple, fast, and very good at finding interesting
rules of a specific kind in baskets or other transaction
data, using operations that are efficient in standard
database systems.
It is used a lot in the R&D Depts of retailers in industry
(or by consultancies who do work for them).
But note that we will now talk about itemsets instead of
rules. Also, the coverage of a rule is the same as the
support of an itemset.
Don’t get confused!
The Apriori Algorithm
15. Find rules in two stages
Agarwal and colleagues divided the problem of
finding good rules into two phases:
1. Find all itemsets with a specified minimal
support (coverage). An itemset is just a specific
set of items, e.g. {apples, cheese}. The Apriori algorithm
can efficiently find all itemsets whose coverage is above
a given minimum.
2. Use these itemsets to help generate
interersting rules. Having done stage 1, we have
considerably narrowed down the possibilities, and can
do reasonably fast processing of the large itemsets to
generate candidate rules.
17. Terminology
k-itemset : a set of k items. E.g.
{beer, cheese, eggs} is a 3-itemset
{cheese} is a 1-itemset
{honey, ice-cream} is a 2-itemset
support: an itemset has support s% if s% of the
records in the DB contain that itemset.
minimum support: the Apriori algorithm starts with
the specification of a minimum level of support,
and will focus on itemsets with this level or above.
18. Terminology
large itemset: doesn’t mean an itemset with many
items. It means one whose support is at least
minimum support.
Lk : the set of all large k-itemsets in the DB.
Ck : a set of candidate large k-itemsets. In the
algorithm we will look at, it generates this set,
which contains all the k-itemsets that might be
large, and then eventually generates the set above.
19. 1 1 1 1 1 1
2 1 1 1
3 1 1 1
4 1 1 1
5 1 1
6 1 1
7 1 1 1
8 1 1
9 1 1
10 1 1
11 1 1
12 1
13 1 1
14 1 1
15 1 1
16 1
17 1 1
18 1 1 1 1 1
19 1 1 1 1 1
20 1
ID a, b, c, d, e, f, g, h, i
E.g.
3-itemset {a,b,h}
has support 15%
2-itemset {a, i}
has support 0%
4-itemset {b, c, d, h}
has support 5%
If minimum support is
10%, then {b} is a
large
itemset, but {b, c, d, h}
Is a small itemset!
20. The Apriori algorithm for finding
large itemsets efficiently in big DBs
1: Find all large 1-itemsets
2: For (k = 2 ; while Lk-1 is non-empty; k++)
3 {Ck = apriori-gen(Lk-1)
4 For each c in Ck, initialise c.count to zero
5 For all records r in the DB
6 {Cr = subset(Ck, r); For each c in Cr , c.count++ }
7 Set Lk := all c in Ck whose count >= minsup
8 } /* end -- return all of the Lk sets.
21. The Apriori algorithm for finding
large itemsets efficiently in big DBs
1: Find all large 1-itemsets
2: For (k = 2 ; while Lk-1 is non-empty; k++)
3 {Ck = apriori-gen(Lk-1)
4 For each c in Ck, initialise c.count to zero
5 For all records r in the DB
6 {Cr = subset(Ck, r); For each c in Cr , c.count++ }
7 Set Lk := all c in Ck whose count >= minsup
8 } /* end -- return all of the Lk sets.
Generate candidate 2-itemsets
Prune them to leave the valid ones
(those with enough support)
22. The Apriori algorithm for finding
large itemsets efficiently in big DBs
1: Find all large 1-itemsets
2: For (k = 2 ; while Lk-1 is non-empty; k++)
3 {Ck = apriori-gen(Lk-1)
4 For each c in Ck, initialise c.count to zero
5 For all records r in the DB
6 {Cr = subset(Ck, r); For each c in Cr , c.count++ }
7 Set Lk := all c in Ck whose count >= minsup
8 } /* end -- return all of the Lk sets.
Generate candidate 3-itemsets
Prune them to leave the valid ones
(those with enough support)
23. The Apriori algorithm for finding
large itemsets efficiently in big DBs
1: Find all large 1-itemsets
2: For (k = 2 ; while Lk-1 is non-empty; k++)
3 {Ck = apriori-gen(Lk-1)
4 For each c in Ck, initialise c.count to zero
5 For all records r in the DB
6 {Cr = subset(Ck, r); For each c in Cr , c.count++ }
7 Set Lk := all c in Ck whose count >= minsup
8 } /* end -- return all of the Lk sets.
Generate candidate 4-itemsets
Prune them to leave the valid ones
(those with enough support)
… etc …
24. Generating candidate itemsets …
Suppose these are the only 3-itemsets all have >10% support:
{a, b, c}
{a, e, g}
{e, f, h}
{e, f, k}
{p, q, r}
.. How do we generate candidate 4-itemsets that might have
10% support?
25. Generating candidate itemsets …
Suppose these are the only 3-itemsets all have >10% support:
{a, b, c}
{a, e, g}
{e, f, h}
{e, f, k}
{p, q, r}
One possibility:
1. note all the items involved:
{a, b, c, e, f, g, h, k, p, q, r}
2. generate all subsets of 4 of these:
{a,b,c,e}, {a,,b,c,f}, {a,b,c,g}, {a,b,c,h},
{a,b,c,k}, {a,b,c, p},… etc … there are
330 possible subsets in this case !
26. Generating candidate itemsets …
Suppose these are the only 3-itemsets all have >10% support:
{a, b, c}
{a, e, g}
{e, f, h}
{e, f, k}
{p, q, r}
One possibility:
1. note all the items involved:
{a, b, c, e, f, g, h, k, p, q, r}
2. generate all subsets of 4 of these:
{a,b,c,e}, {a,b,c,f}, {a,b,c,g}, {a,b,c,h},
{a,b,c,k}, {a,b,c, p},… etc … there are
330 possible subsets in this case !
But, hold on: we can easily see that {a,b,c,e} couldn’t have
10% support – because {a,b,e} is not one of our 3-itsemsets
27. Generating candidate itemsets …
Suppose these are the only 3-itemsets all have >10% support:
{a, b, c}
{a, e, g}
{e, f, h}
{e, f, k}
{p, q, r}
One possibility:
1. note all the items involved:
{a, b, c, e, f, g, h, k, p, q, r}
2. generate all subsets of 4 of these:
{a,b,c,e}, {a,b,c,f}, {a,b,c,g}, {a,b,c,h},
{a,b,c,k}, {a,b,c, p},… etc … there are
330 possible subsets in this case !
But, hold on: the same goes for several other of these subsets
…
28. A neat Apriori trick
{a, b, c}
{a, e, g}
{e, f, h}
{e, f, k}
{p, q, r}
i. enforce that subsets are always arranged
‘lexicographically’ (or similar), as they are
already on the left
ii. Only generate k+1-itemset candidates from
k-itemsets that differ in the last item.
So, in this case, the only candidate 4-itemset
would be:
{e, f, h, k}
29. A neat Apriori trick
{a, b, c, e}
{a, e, g, r}
{a, e, g, w}
{e, f, k, p}
{n, q, r, t }
{n, q, r, v }
{n, q, s, v }
i. enforce that subsets are always arranged
‘lexicographically’ (or similar), as they are
already on the left
ii. Only generate k+1-itemset candidates from
k-itemsets that differ in the last item.
And in this case, the only candidate 5-itemsets
would be:
{a, e, g, r, w}, {n, q, r, t, v}
30. A neat Apriori trick
This trick
• guarantees to capture the itemsets that have enough support,
• will still generate some candidates that don’t have enough
support, so we still have to check them in the ‘pruning’ step,
• is particularly convenient for implementation in a standard
relational style transaction database; it is a certain type of ‘self-
Join’ operation.
31. Explaining the Apriori Algorithm …
1: Find all large 1-itemsets
To start off, we simply find all of the large
1-itemsets. This is done by a basic scan of the
DB. We take each item in turn, and count the
number of times that item appears in a basket. In
our running example, suppose minimum support
was 60%, then the only large 1-itemsets would
be: {a}, {b}, {c}, {d} and {f}. So we get
L1 = { {a}, {b}, {c}, {d}, {f}}
32. Explaining the Apriori Algorithm …
1: Find all large 1-itemsets
2: For (k = 2 ; while Lk-1 is non-empty; k++)
We already have L1. This next bit just means that the
remainder of the algorithm generates L2, L3 , and so on
until we get to an Lk that’s empty.
How these are generated is like this:
33. Explaining the Apriori Algorithm …
1: Find all large 1-itemsets
2: For (k = 2 ; while Lk-1 is non-empty; k++)
3 {Ck = apriori-gen(Lk-1)
Given the large k-1-itemsets, this step generates some
candidate k-itemsets that might be large. Because of how
apriori-gen works, the set Ck is guaranteed to
contain all the large k-itemsets, but also contains some
that will turn out not to be `large’.
34. Explaining the Apriori Algorithm …
1: Find all large 1-itemsets
2: For (k = 2 ; while Lk-1 is non-empty; k++)
3 {Ck = apriori-gen(Lk-1)
4 For each c in Ck, initialise c.count to zero
5 For all records r in the DB
6 {Cr = subset(Ck, r); For each c in Cr , c.count++ }
7 Set Lk := all c in Ck whose count >= minsup
Here, we are simply scanning through the DB to count
the support for each of our candidates in Ck , throwing
out the ones without enough support, and the rest become
our set of ‘large’ k-itemsets, Lk
35. Explaining the Apriori Algorithm …
1: Find all large 1-itemsets
2: For (k = 2 ; while Lk-1 is non-empty; k++)
3 {Ck = apriori-gen(Lk-1)
4 For each c in Ck, initialise c.count to zero
5 For all records r in the DB
6 {Cr = subset(Ck, r); For each c in Cr , c.count++ }
7 Set Lk := all c in Ck whose count >= minsup
8 } /* end -- return all of the Lk sets.
We finish at the point where we get an empty Lk .
The algorithm returns all of the (non-empty) Lk sets, from L1 through to
whichever was the last non-empty one. This gives us an excellent start in
finding interesting rules (although the large itemsets themselves will
usually be very interesting and useful).
36. From itemsets to rules
The Apriori algorithm finds interesting (i.e. frequent) itemsets.
E.g. it may find that {apples, bananas, milk} has coverage 30%
-- so 30% of transactions contain each of these three things.
What can you say about the coverage of {apples, milk}?
We can invent several potential rules, e.g.:
IF basket contains apples and bananas, it also contains MILK.
Suppose support of {a, b} is 40%; what is the confidence of this rule?
37. What this lecture was about
• The Apriori algorithm for efficiently
finding frequent large itemsets in large
DBs
• Associated terminology
• Associated notes about rules, and working
out the confidence of a rule based on the
support of its component itemsets
39. A full run through of Apriori
1 1 1 1 1
2 1 1 1
3 1 1 1
4 1 1
5 1 1 1
6 1
7 1 1 1
8 1
9 1 1
10 1 1
11 1 1 1
12 1
13 1 1
14 1 1 1 1
15
16 1
17 1 1 1
18 1 1 1 1
19 1 1 1 1 1
20 1
ID a, b, c, d, e, f, g
We will assume this is
our transaction database
D and we will assume
minsup is 4 (20%)
This will not be run
through in the lecture; it is
here to help with revision
40. First we find all the large 1-itemsets. I.e., in this case, all the
1-itemsets that are contained by at least 4 records in the DB.
In this example, that’s all of them. So,
L1 = {{a}, {b}, {c}, {d}, {e}, {f}, {g}}
Now we set k = 2 and run apriori-gen to generate C2
The join step when k=2 just gives us the set of all alphabetically
ordered pairs from L1, and we cannot prune any away, so we
have C2 = {{a, b}, {a, c}, {a, d}, {a, e}, {a, f}, {a, g}, {b, c},
{b, d}, {b, e}, {b, f}, {b, g}, {c, d}, {c, e}, {c, f}, {c, g},
{d, e}, {d, f}, {d, g}, {e, f}, {e, g}, {f, g}}
41. So we have
C2 = {{a, b}, {a, c}, {a, d}, {a, e}, {a, f}, {a, g}, {b, c}, {b, d}, {b, e}, {b, f}, {b, g}, {c, d},
{c, e}, {c, f}, {c, g}, {d, e}, {d, f}, {d, g}, {e, f}, {e, g}, {f, g}}
Line 4 of the Apriori algorithm now tells us set a counter for
each of these to 0. Line 5 now prepares us to take each record in
the DB in turn, and find which of those in C2 are contained in it.
The first record r1 is: {a, b, d, g}. Those of C2 it contains are:
{a, b}, {a, d}, {a, g}, {a, d}, {a, g}, {b, d}, {b, g}, {d, g}.
Hence Cr1 = {{a, b}, {a, d}, {a, g}, {a, d}, {a, g}, {b, d}, {b, g}, {d, g}}
and the rest of line 6 tells us to increment the counters of these itemsets.
The second record r2 is:{c, d, e}; Cr2 = {{c, d}, {c, e}, {d, e}},
and we increment the counters for these three itemsets.
…
After all 20 records, we look at the counters, and in this case we will find
that the itemsets with >= minsup (4) counters are: {a, d}, {c, e}.
So, L2 = {{a, c}, {a, d}, {c, d}, {c, e}, {c, f}}
42. So we have L2 = {{a, c}, {a, d}, {c, d}, {c, e}, {c, f}}
We now set k = 3 and run apriori-gen on L2 .
The join step finds the following pairs that meet the
required pattern: {a, c}:{a, d} {c, d}:{c, e} {c, d}:{c, f} {c, e}:{c, f}
This leads to the candidates 3-itemsets:
{a, c, d}, {c, d, e}, {c, d, f}, {c, e, f}
We prune {c, d, e} since {d, e} is not in L2
We prune {c, d, f} since {d, f} is not in L2
We prune {c, e, f} since {e, f} is not in L2
We are left with C3 = {a, c, d}
We now run lines 5—7, to count how many records contain
{a, c, d}. The count is 4, so L3 = {a, c, d}
43. So we have L3 = {a, c, d}
We now set k = 4, but when we run apriori-gen on L3 we get the
empty set, and hence eventually we find L4 = {}
This means we now finish, and return the set of all of the non-
empty Ls – these are all of the large itemsets:
Result = {{a}, {b}, {c}, {d}, {e}, {f}, {g}, {a, c}, {a, d}, {c, d}, {c, e},
{c, f}, {a, c, d}}
Each large itemset is intrinsically interesting, and may be of business value.
Simple rule-generation algorithms can now use the large itemsets as a starting
point.
44. Test yourself: Understanding rules
Suppose itemset A = {beer, cheese, eggs} has 30% support in the DB
{beer, cheese} has 40%, {beer, eggs} has 30%, {cheese, eggs} has 50%,
and each of beer, cheese, and eggs alone has 50% support..
What is the confidence of:
IF basket contains Beer and Cheese, THEN basket also contains Eggs ?
The confidence of a rule if A then B is simply:
support(A + B) / support(A).
So it’s 30/40 = 0.75 ; this rule has 75% confidence
What is the confidence of:
IF basket contains Beer, THEN basket also contains Cheese and Eggs ?
30 / 50 = 0.6 so this rule has 60% confidence
The answers are in the above boxes in white font colour
45. Test yourself: Understanding rules
If the following rule has confidence c: If A then B
and if support(A) = 2 * support(B), what can be said
about the confidence of: If B then A
confidence c is support(A + B) / support(A)
= support(A + B) / 2 * support(B)
Let d be the confidence of ``If B then A’’.
d is support(A+B / support(B) -- Clearly, d = 2c
E.g. A might be milk and B might be newspapers
The answers are in the above box in white font colour