This document discusses association rule mining techniques for extracting useful patterns from large datasets. It provides background on association rule mining and defines key concepts like support, confidence and frequent itemsets. The document then reviews several classic association rule mining algorithms like AIS, Apriori and FP-Growth. It explains that these algorithms aim to improve quality and efficiency by reducing database scans, generating fewer candidate itemsets and using pruning techniques.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Multiple Minimum Support Implementations with Dynamic Matrix Apriori Algorith...ijsrd.com
Data mining can be defined as the process of uncovering hidden patterns in random data that are potentially useful. The discovery of interesting association relationships among large amounts of business transactions is currently vital for making appropriate business decisions. Association rule analysis is the task of discovering association rules that occur frequently in a given transaction data set. Its task is to find certain relationships among a set of data (itemset) in the database. It has two measurements: Support and confidence values. Confidence value is a measure of rule’s strength, while support value corresponds to statistical significance. There are currently a variety of algorithms to discover association rules. Some of these algorithms depend on the use of minimum support to weed out the uninteresting rules. Other algorithms look for highly correlated items, that is, rules with high confidence. Traditional association rule mining techniques employ predefined support and confidence values. However, specifying minimum support value of the mined rules in advance often leads to either too many or too few rules, which negatively impacts the performance of the overall system. This work proposes a way to efficiently mine association rules over dynamic databases using Dynamic Matrix Apriori technique and Multiple Support Apriori (MSApriori). A modification for Matrix Apriori algorithm to accommodate this modification is proposed. Experiments on large set of data bases have been conducted to validate the proposed framework. The achieved results show that there is a remarkable improvement in the overall performance of the system in terms of run time, the number of generated rules, and number of frequent items used.
Data Mining is an important aspect for any business. Most of the management level decisions are based on the process of Data Mining. One of such aspect is the association between different sale products i.e. what is the actual support of a product respected to the other product. This concept is called Association Mining. According to this concept we define the process of estimating the sale of one product respective to the other product. We are proposing an association rule based on the concept of Hardware support. In this concept we first maintain the database and compare it with systolic array after this a pruning process is being performed to filter the database and to remove the rarely used items. Finally the data is indexed according to hashing technique and the decision is performed in terms of support count. Krishan Rohilla | Shabnam Kumari | Reema"Data Mining based on Hashing Technique" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: http://www.ijtsrd.com/papers/ijtsrd82.pdf http://www.ijtsrd.com/computer-science/data-miining/82/data-mining-based-on-hashing-technique/krishan-rohilla
Review on: Techniques for Predicting Frequent Itemsvivatechijri
Electronic commerce(E- Commerce) is the trading or facilitation of trading in products or services
using computer networks, such as the Internet. It comes under a part of Data Mining which takes large amount
of data and extracts them. The paper uses the information about the techniques and methods used in the
shopping cart for prediction of product that the customer wants to buy or will buy and shows the relevant
products according to the cost of the product. The paper also summarizes the descriptive methods with
examples. For predicting the frequent pattern of itemset, many prediction algorithms, rule mining techniques
and various methods have already been designed for use of retail market. This paper examines literature
analysis on several techniques for mining frequent itemsets.The survey comprises various tree formations like
Partial tree, IT tree and algorithms with its advantages and its limitations.
The development of data mining is inseparable from the recent developments in information technology that enables the accumulation of large amounts of data. For example, a shopping mall that records every sales transaction of goods using various POS (point of sales). Database data from these sales could reach a large storage capacity, even more being added each day, especially when the shopping center will develop into a nationwide network. The development of the internet at the moment also has a share large enough in the accumulation of data occurs. But the rapid growth of data accumulation it has created conditions that are often referred to as "data rich but information poor" because the data collected can not be used optimally for useful applications. Not infrequently the data set was left just seemed to be a "grave data". There are several techniques used in data mining which includes association, classification, and clustering. In this paper, the author will do a comparison between the performance of the technical classification methods naïve Bayes and C4.5 algorithms.
An Efficient Compressed Data Structure Based Method for Frequent Item Set Miningijsrd.com
Frequent pattern mining is very important for business organizations. The major applications of frequent pattern mining include disease prediction and analysis, rain forecasting, profit maximization, etc. In this paper, we are presenting a new method for mining frequent patterns. Our method is based on a new compact data structure. This data structure will help in reducing the execution time.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Multiple Minimum Support Implementations with Dynamic Matrix Apriori Algorith...ijsrd.com
Data mining can be defined as the process of uncovering hidden patterns in random data that are potentially useful. The discovery of interesting association relationships among large amounts of business transactions is currently vital for making appropriate business decisions. Association rule analysis is the task of discovering association rules that occur frequently in a given transaction data set. Its task is to find certain relationships among a set of data (itemset) in the database. It has two measurements: Support and confidence values. Confidence value is a measure of rule’s strength, while support value corresponds to statistical significance. There are currently a variety of algorithms to discover association rules. Some of these algorithms depend on the use of minimum support to weed out the uninteresting rules. Other algorithms look for highly correlated items, that is, rules with high confidence. Traditional association rule mining techniques employ predefined support and confidence values. However, specifying minimum support value of the mined rules in advance often leads to either too many or too few rules, which negatively impacts the performance of the overall system. This work proposes a way to efficiently mine association rules over dynamic databases using Dynamic Matrix Apriori technique and Multiple Support Apriori (MSApriori). A modification for Matrix Apriori algorithm to accommodate this modification is proposed. Experiments on large set of data bases have been conducted to validate the proposed framework. The achieved results show that there is a remarkable improvement in the overall performance of the system in terms of run time, the number of generated rules, and number of frequent items used.
Data Mining is an important aspect for any business. Most of the management level decisions are based on the process of Data Mining. One of such aspect is the association between different sale products i.e. what is the actual support of a product respected to the other product. This concept is called Association Mining. According to this concept we define the process of estimating the sale of one product respective to the other product. We are proposing an association rule based on the concept of Hardware support. In this concept we first maintain the database and compare it with systolic array after this a pruning process is being performed to filter the database and to remove the rarely used items. Finally the data is indexed according to hashing technique and the decision is performed in terms of support count. Krishan Rohilla | Shabnam Kumari | Reema"Data Mining based on Hashing Technique" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: http://www.ijtsrd.com/papers/ijtsrd82.pdf http://www.ijtsrd.com/computer-science/data-miining/82/data-mining-based-on-hashing-technique/krishan-rohilla
Review on: Techniques for Predicting Frequent Itemsvivatechijri
Electronic commerce(E- Commerce) is the trading or facilitation of trading in products or services
using computer networks, such as the Internet. It comes under a part of Data Mining which takes large amount
of data and extracts them. The paper uses the information about the techniques and methods used in the
shopping cart for prediction of product that the customer wants to buy or will buy and shows the relevant
products according to the cost of the product. The paper also summarizes the descriptive methods with
examples. For predicting the frequent pattern of itemset, many prediction algorithms, rule mining techniques
and various methods have already been designed for use of retail market. This paper examines literature
analysis on several techniques for mining frequent itemsets.The survey comprises various tree formations like
Partial tree, IT tree and algorithms with its advantages and its limitations.
The development of data mining is inseparable from the recent developments in information technology that enables the accumulation of large amounts of data. For example, a shopping mall that records every sales transaction of goods using various POS (point of sales). Database data from these sales could reach a large storage capacity, even more being added each day, especially when the shopping center will develop into a nationwide network. The development of the internet at the moment also has a share large enough in the accumulation of data occurs. But the rapid growth of data accumulation it has created conditions that are often referred to as "data rich but information poor" because the data collected can not be used optimally for useful applications. Not infrequently the data set was left just seemed to be a "grave data". There are several techniques used in data mining which includes association, classification, and clustering. In this paper, the author will do a comparison between the performance of the technical classification methods naïve Bayes and C4.5 algorithms.
An Efficient Compressed Data Structure Based Method for Frequent Item Set Miningijsrd.com
Frequent pattern mining is very important for business organizations. The major applications of frequent pattern mining include disease prediction and analysis, rain forecasting, profit maximization, etc. In this paper, we are presenting a new method for mining frequent patterns. Our method is based on a new compact data structure. This data structure will help in reducing the execution time.
Data Mining For Supermarket Sale Analysis Using Association Ruleijtsrd
Data mining is the novel technology of discovering the important information from the data repository which is widely used in almost all fields Recently, mining of databases is very essential because of growing amount of data due to its wide applicability in retail industries in improving marketing strategies. Analysis of past transaction data can provide very valuable information on customer behavior and business decisions. The amount of data stored grows twice as fast as the speed of the fastest processor available to analyze it.Its main purpose is to find the association relationship among the large number of database items. It is used to describe the patterns of customers purchase in the supermarket. This is presented in this paper. Rajeshri Shelke"Data Mining For Supermarket Sale Analysis Using Association Rule" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: http://www.ijtsrd.com/papers/ijtsrd94.pdf http://www.ijtsrd.com/engineering/computer-engineering/94/data-mining-for-supermarket-sale-analysis-using-association-rule/rajeshri-shelke
A Quantified Approach for large Dataset Compression in Association MiningIOSR Journals
Abstract: With the rapid development of computer and information technology in the last several decades, an
enormous amount of data in science and engineering will continuously be generated in massive scale; data
compression is needed to reduce the cost and storage space. Compression and discovering association rules by
identifying relationships among sets of items in a transaction database is an important problem in Data Mining.
Finding frequent itemsets is computationally the most expensive step in association rule discovery and therefore
it has attracted significant research attention. However, existing compression algorithms are not appropriate in
data mining for large data sets. In this research a new approach is describe in which the original dataset is
sorted in lexicographical order and desired number of groups are formed to generate the quantification tables.
These quantification tables are used to generate the compressed dataset, which is more efficient algorithm for
mining complete frequent itemsets from compressed dataset. The experimental results show that the proposed
algorithm performs better when comparing it with the mining merge algorithm with different supports and
execution time.
Keywords: Apriori Algorithm, mining merge Algorithm, quantification table
In today’s world there is a wide availability of huge amount of data and thus there is a need for turning this
data into useful information which is referred to as knowledge. This demand for knowledge discovery
process has led to the development of many algorithms used to determine the association rules. One of the
major problems faced by these algorithms is generation of candidate sets. The FP-Tree algorithm is one of
the most preferred algorithms for association rule mining because it gives association rules without
generating candidate sets. But in the process of doing so, it generates many CP-trees which decreases its
efficiency. In this research paper, an improvised FP-tree algorithm with a modified header table, along
with a spare table and the MFI algorithm for association rule mining is proposed. This algorithm generates
frequent item sets without using candidate sets and CP-trees.
COMPARATIVE STUDY OF DISTRIBUTED FREQUENT PATTERN MINING ALGORITHMS FOR BIG S...IAEME Publication
Association rule mining plays an important role in decision support system. Nowadays in the era of internet, various online marketing sites and social networking sites are generating enormous amount of structural/semi structural data in the form of sales data, tweets, emails, web pages and so on. This online generated data is too large that it becomes very complex to process and analyze it using traditional systems which consumes more time. This paper overcomes the main memory bottleneck in single computing system. There are two major goals of this paper. In this paper, big sales dataset of AMUL dairy is preprocessed using Hadoop Map Reduce that convert it into the transactional dataset. Then, after removing the null transactions; distributed frequent pattern mining algorithm MR-DARM (Map Reduce based Distributed Association Rule Mining) is used to find most frequent item set. Finally, strong association rules are generated from frequent item sets. The paper also compares the time efficiency of MR-DARM algorithm with existing Count Distributed Algorithm (CDA) and Fast Distributed Mining (FDM) distributed frequent pattern mining algorithms. The compared algorithms are presented together with experimental results that lead to the final conclusions.
Data Mining For Supermarket Sale Analysis Using Association Ruleijtsrd
Data mining is the novel technology of discovering the important information from the data repository which is widely used in almost all fields Recently, mining of databases is very essential because of growing amount of data due to its wide applicability in retail industries in improving marketing strategies. Analysis of past transaction data can provide very valuable information on customer behavior and business decisions. The amount of data stored grows twice as fast as the speed of the fastest processor available to analyze it.Its main purpose is to find the association relationship among the large number of database items. It is used to describe the patterns of customers purchase in the supermarket. This is presented in this paper. Rajeshri Shelke"Data Mining For Supermarket Sale Analysis Using Association Rule" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: http://www.ijtsrd.com/papers/ijtsrd94.pdf http://www.ijtsrd.com/engineering/computer-engineering/94/data-mining-for-supermarket-sale-analysis-using-association-rule/rajeshri-shelke
A Quantified Approach for large Dataset Compression in Association MiningIOSR Journals
Abstract: With the rapid development of computer and information technology in the last several decades, an
enormous amount of data in science and engineering will continuously be generated in massive scale; data
compression is needed to reduce the cost and storage space. Compression and discovering association rules by
identifying relationships among sets of items in a transaction database is an important problem in Data Mining.
Finding frequent itemsets is computationally the most expensive step in association rule discovery and therefore
it has attracted significant research attention. However, existing compression algorithms are not appropriate in
data mining for large data sets. In this research a new approach is describe in which the original dataset is
sorted in lexicographical order and desired number of groups are formed to generate the quantification tables.
These quantification tables are used to generate the compressed dataset, which is more efficient algorithm for
mining complete frequent itemsets from compressed dataset. The experimental results show that the proposed
algorithm performs better when comparing it with the mining merge algorithm with different supports and
execution time.
Keywords: Apriori Algorithm, mining merge Algorithm, quantification table
In today’s world there is a wide availability of huge amount of data and thus there is a need for turning this
data into useful information which is referred to as knowledge. This demand for knowledge discovery
process has led to the development of many algorithms used to determine the association rules. One of the
major problems faced by these algorithms is generation of candidate sets. The FP-Tree algorithm is one of
the most preferred algorithms for association rule mining because it gives association rules without
generating candidate sets. But in the process of doing so, it generates many CP-trees which decreases its
efficiency. In this research paper, an improvised FP-tree algorithm with a modified header table, along
with a spare table and the MFI algorithm for association rule mining is proposed. This algorithm generates
frequent item sets without using candidate sets and CP-trees.
COMPARATIVE STUDY OF DISTRIBUTED FREQUENT PATTERN MINING ALGORITHMS FOR BIG S...IAEME Publication
Association rule mining plays an important role in decision support system. Nowadays in the era of internet, various online marketing sites and social networking sites are generating enormous amount of structural/semi structural data in the form of sales data, tweets, emails, web pages and so on. This online generated data is too large that it becomes very complex to process and analyze it using traditional systems which consumes more time. This paper overcomes the main memory bottleneck in single computing system. There are two major goals of this paper. In this paper, big sales dataset of AMUL dairy is preprocessed using Hadoop Map Reduce that convert it into the transactional dataset. Then, after removing the null transactions; distributed frequent pattern mining algorithm MR-DARM (Map Reduce based Distributed Association Rule Mining) is used to find most frequent item set. Finally, strong association rules are generated from frequent item sets. The paper also compares the time efficiency of MR-DARM algorithm with existing Count Distributed Algorithm (CDA) and Fast Distributed Mining (FDM) distributed frequent pattern mining algorithms. The compared algorithms are presented together with experimental results that lead to the final conclusions.
Presentation on radiographic representation of pulmonary tuberculosis with specific role of CT scan. Special presentation presented on world TB day at Baqai University Karachi Pakistan
Studi Kasus Penyakit Typoid Di RSUD Banjarbaru Kalimantan Selatan berikut ini adalah tugas mata kuliah epidemiologi asuhan Prof. Dr Qomariyatus Sholihah, Amd. Hyp,ST,.M.Kes yang disusun oleh mahasiswa dan mahasiswi Program Studi Teknik Lingkungan Universitas Lambung Mangkurat yakni Afresa Amanda, Lianatul Munjiah, Maria Septia Memorini, dan Rinaldy Kusuma SY
Grow Your Email List - From where it is to where you want it to beBria Sullivan
It’s no secret that email marketing works. But how do you get to a point where you have a list of email addresses to start marketing to? Where do you start to look to gather contacts of people interested in your organization? And how do you keep it all organized? If you’ve ever asked yourself these questions, you don’t want to miss this. We’ll dive into some easy ways to gather contacts wherever you go, and we’ll give you some tips for keeping contacts organized and segmented for easier marketing.
In this presentation you will learn how to:
• Get started with gathering email contacts
• Establish an organized contact segmentation process
• Grow your email lists online and in person
• Keep your subscribers interested after they sign up
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
CONFIGURING ASSOCIATIONS TO INCREASE TRUST IN PRODUCT PURCHASEIJwest
Clustering is categorizing data into groups with similar objects. Data mining adds to complexities of clustering a large dataset with various features. Among these datasets, there are electronic business stores which offer their products through web. These stores require recommendation systems which can offer products to the user which the user might require them with higher probability. In this study, previous purchases of users are used to present a sorted list of products to the user. Identifying associations related to users and finding centers increases precision of the recommended list. Configuration of associations and creating a profile for users is important in current studies. In the proposed method, association rules are presented to model user interactions in the web which use time that a page is visited and frequency of visiting a page to weight pages and describes users’ interest to page groups. Therefore, weight of each transaction item describes user’s interest in that item. Analyzing results show that the proposed method presents a more complete model of users’ behavior because it combines weight and membership degree of pages simultaneously for ranking candidate pages. This method has obtained higher accuracy compared to other methods even in higher number of pages.
Configuring Associations to Increase Trust in Product Purchase dannyijwest
Clustering is categorizing data into groups with similar objects. Data mining adds to complexities of clustering a large dataset with various features. Among these datasets, there are electronic business stores which offer their products through web. These stores require recommendation systems which can offer products to the user which the user might require them with higher probability. In this study, previous purchases of users are used to present a sorted list of products to the user. Identifying associations related to users and finding centers increases precision of the recommended list. Configuration of associations and creating a profile for users is important in current studies. In the proposed method, association rules are presented to model user interactions in the web which use time that a page is visited and frequency of visiting a page to weight pages and describes users’ interest to page groups. Therefore, weight of each transaction item describes user’s interest in that item. Analyzing results show that the proposed method presents a more complete model of users’ behavior because it combines weight and membership degree of pages simultaneously for ranking candidate pages. This method has obtained higher accuracy compared to other methods even in higher number of pages.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Efficient Approach for Asymmetric Data ClassificationAM Publications
In many classification problems, the number of targets (eg. intruders) present is very small compared with
the number of clutter objects. Traditional classification approaches usually ignore this class imbalance, causing
performance to experience low accordingly. In contrary, the algorithm considerably imbalanced logistic regression
(IILR) algorithm explicitly addresses class imbalance in its formulation. I am proposing this algorithm and give the
details necessary to employ it for intrusion detection data sets characterized by class imbalance.
BINARY DECISION TREE FOR ASSOCIATION RULES MINING IN INCREMENTAL DATABASESIJDKP
This research paper proposes an algorithm to find association rules for incremental databases. Most of the
transaction databases are often dynamic. Suppose consider super market customers daily purchase
transactions. Day to day customer’s behaviour to purchase items may change and new products replace
old products. In this scenario static data mining algorithms doesn't make good significance. If an algorithm
continuously learns day to day, then we can get most updated knowledge. This is very much helpful in
present fast updating world. Famous and benchmarked algorithms for Association rules mining are
Apriory and FP- Growth. However, the major drawback in Appriory and FP-Growth is, they must be
rebuilt all over again once the original database is changed. Therefore, in this paper we introduce an
efficient algorithm called Binary Decision Tree (BDT) to process incremental data. To process
continuously data we need so much of processing and storage resources. In this algorithm we scan data
base only once by which we construct dynamic growing binary tree to find association rules with better
performance and optimum storage. We can apply for static data also, but our main intension is to give
optimum solution for incremental data.
BINARY DECISION TREE FOR ASSOCIATION RULES MINING IN INCREMENTAL DATABASES IJDKP
This research paper proposes an algorithm to find association rules for incremental databases. Most of the
transaction databases are often dynamic. Suppose consider super market customers daily purchase
transactions. Day to day customer’s behaviour to purchase items may change and new products replace
old products. In this scenario static data mining algorithms doesn't make good significance. If an algorithm
continuously learns day to day, then we can get most updated knowledge. This is very much helpful in
present fast updating world. Famous and benchmarked algorithms for Association rules mining are
Apriory and FP- Growth. However, the major drawback in Appriory and FP-Growth is, they must be
rebuilt all over again once the original database is changed. Therefore, in this paper we introduce an
efficient algorithm called Binary Decision Tree (BDT) to process incremental data. To process
continuously data we need so much of processing and storage resources. In this algorithm we scan data
base only once by which we construct dynamic growing binary tree to find association rules with better
performance and optimum storage. We can apply for static data also, but our main intension is to give
optimum solution for incremental data.
NEW ALGORITHM FOR SENSITIVE RULE HIDING USING DATA DISTORTION TECHNIQUEcscpconf
Data mining is the process of extracting hidden patterns of data. Association rule mining is an
important data mining task that finds interesting association among a large set of data item. It
may disclose pattern and various kinds of sensitive information. Such information may be
protected against unauthorized access. Association rule hiding is one of the techniques of
privacy preserving data mining to protect the association rules generated by association rule
mining. This paper adopts data distortion technique for hiding sensitive association rules.
Algorithms based on this technique either hide a specific rule using data alteration technique or
hide the rules depending on the sensitivity of the items to be hidden. In the proposed technique,
positions of sensitive items are altered while maintaining the support. The proposed technique
uses the idea of representative rules to prune the rules first and then hides the sensitive rules.
Introduction To Multilevel Association Rule And Its MethodsIJSRD
Association rule mining is a popular and well researched method for discovering interesting relations between variables in large databases. In this paper we introduce the concept of Data mining, Association rule and Multilevel association rule with different algorithm, its advantage and concept of Fuzzy logic and Genetic Algorithm. Multilevel association rules can be mined efficiently using concept hierarchies under a support-confidence framework.
Comparative study of frequent item set in data miningijpla
In this paper, we are an overview of already presents frequent item set mining algorithms. In these days
frequent item set mining algorithm is very popular but in the frequent item set mining computationally
expensive task. Here we described different process which use for item set mining, We also compare
different concept and algorithm which used for generation of frequent item set mining From the all the
types of frequent item set mining algorithms that have been developed we will compare important ones. We
will compare the algorithms and analyze their run time performance.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Quality defects in TMT Bars, Possible causes and Potential Solutions.
A literature review of modern association rule mining techniques
1. International Journal of Current Trends in Engineering & Technology
ISSN: 2395-3152
Volume: 02, Issue: 02 (MAR-APR, 2016)
213
A Literature Review of Modern Association Rule Mining Techniques
Rupa Rajoriya, Prof. Kailash Patidar
Computer Science & engineering
SSSIST Sehore, India
rprajoriya21@gmail.com
Abstract:-Data mining is a technique that helps to
extract important data from a large database. It is the
process of sorting through large amounts of data and
picking out relevant information through the use of
certain sophisticated algorithms. As a lot of information
is gathered, with the quantity of information doubling
each three years, data mining is becoming a vital tool to
rework this information into data One common method to
extract useful patterns from data is association rule mining. It
is used in n number of applications. But the problem with the
existing association rule mining methods is that they generate
rules by taking more time and space. This becomes a real
problem when rules are to be mined from a large data set.
Keyword:-FP Tree, Association Rule, Warehouse, Mining.
KDD Processes.
I. INTRODUCTION
In data mining, association rule learning is a widespread
and well researched technique for locating interesting
relations between variables in massive databases. It is meant
to spot robust rules discovered in databases using different
measures of power. Based on the conception of robust rules,
[1] introduced association rules for locating regularities
between merchandise in large-scale dealing knowledge noted
by point of sale (POS) systems in supermarkets. For example,
the rule → found within the sales knowledge of a food market
would indicate that if a client buys onions and potatoes along,
he or she is probably going to additionally get hamburger
meat. Such information is often used because the basis for
decisions regarding marketing activity likes e.g. promotional
evaluation or product placements. In addition to the above
example from market basket analysis association rules are
used these days in several application areas as well as web
usage mining, bioinformatics and intrusion detection. As
against sequence mining, association rule learning generally
doesn't take into account the order of things either inside a
transaction or across transactions.
Fig.1 Concept of Mining Association Rule
Actual process work as follows. First we need to clean
and integrate the databases. Since the data source may
come from different databases, which may have some
inconsistence and duplications, we must clean the data
source by removing those noises or make some
compromises. Suppose we have two different
databases, different words are used to refer the same
thing in their schema. When we try to unite the two
sources we can only choose one of them, if we know that
they indicate the same thing. And also real world data
tend to be incomplete and noisy due to the manual input
mistakes. The integrated data sources can be stored in a
database, data warehouse or other repositories. As not all
the data in the database are related to our mining task, the
second process is to select task related data from the
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ISSN: 2395-3152
Volume: 02, Issue: 02 (MAR-APR, 2016)
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integrated resources and transform them into a format that
is ready to be mined. Data mining is generally thought of
as the process of finding hidden, non-trivial and previously
unknown information in large collection of data.
Fig. 2 Knowledge Discovery in Database processes
Association rule mining is an essential component of data
mining. Basic objective of finding association rules is to
find all co-occurrence relationship called associations. Most
of the research efforts in the scope of association rules
have been oriented to simplify the rule set and to improve
performance of algorithm. But these are not the only
problems that can be found and when rules are generated
II. RELATED WORK
The chapter deals with relevant literatures review of
various topics that fall under data mining a nd
association rules. The first part discuses mining
association rules definition and concept, and lastly some of
the well-known data mining algorithms along with their
computational difficulty.
III. ASSOCIATION RULE MINING
Association rule mining, one of the most important and
well researched methods of data mining, introduced by [1].
Its objective is to extract interesting frequent patterns,
correlations, associations or casual structures among sets of
items in the transaction databases, data warehouse or
other data repositories. Association mining is a
fundamental data mining technique. It identifies
items that are associated with one another in data. The
problem of association rule mining is stated as follows.
Let me= {a1, a2….an} be a finite set of items. A
transaction database is a set of transactions T= {t1, t2...tm}
where each transaction j⊂I (1≤ j≤ m) represents a set of
items purchased by a customer at a given time. An
itemset is a set of items A⊂I. The support of an itemset is
denoted as sup (A) and is denoted as the number or
transaction that contain A. An association rule A→ I is a
relationship between two itemsets X, Y such that X, YI and
X∩Y=Φ. In a database of transactions D with a set of n
binary attributes (items) I, a rule is defined as an
implication of the form X =>Y where X, Y are items and
X∩Y =NULL the sets of items (for short item sets) X and
Y are called antecedent (left-hand-side or LHS) and
consequent (right-hand-side or RHS) of the rule
respectively. Essentially, association mining is about
discovering a set of rules that is shared among a large
percentage of the data [11]. Association rules mining tend
to produce a large amount of rules. The objective is to
find the rules that are useful to users. There are two ways
of computing usefulness, being subjectively and
objectively. Objective measures involve statistical analysis
of the data, such as support and confidence [1]. The
Support, sup(X), of an item set X is defined as the
proportion of transactions in the data set which contain the
item set. Or support can be define as the rule X =>Y carries
with support s if s% of transactions in D contain X U Y.
Rules that have as greater than a user-specified support is
said to have minimum support. The Confidence of a rule is
defined as conf(X =>Y) = supp(X UY) / supp(X). Or
confidence can be define as the rule X=>Y holds with
confidence c if c% of the transactions in D that contain X
also contain Y .Rules that have a c greater than a user-
specified confidence is said to have minimum confidence.
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Association rule mining finds frequent patterns,
correlations, associations or causal structures among sets
of items or objects in transaction database, relational
database, and other information repositories. The key
step of association rule mining is to discover frequent
itemset. Frequent patterns are pattern that occurs
frequently in a database.
Table 3.1: Transaction Table
The other definition of pattern defines it is a form,
template, or model (or, more ideally, a set of rules) which
can be used to make or to create things or parts of a thing.
In data mining we can say that a pattern is a particular data
behavior, arrangement or form that might be of a business
interest. And an item set means a set of items or a group of
elements that represents together a single entity. First we will
introduce some naive and basic algorithms for association
rule mining, Apriori series approaches. Then another
milestone, tree structured approaches will be explained.
Finally this section will end with some special issues of as-
association rule mining, including multiple level ARM,
multiple dimension ARM, constraint based ARM and
incremental ARM.
A. AIS Algorithm focuses on improving the quality of
databases together with necessary functionality to process
decision support queries. In this algorithm only one item
consequent association rules are generated, which means
that the consequent of those rules only contain one item,
for example we only generate rules like X ∩ Y -> Z but
not those rules as X -> Y ∩ Z. The databases were
scanned many times to get the frequent itemsets in AIS.
The main drawback of the AIS algorithm is too many
candidate itemsets that finally turned out to be small are
generated, which requires more space and wastes much
effort that turned out to be useless. At the same time this
algorithm requires too many passes over the whole
database.
B. Apriori Algorithm. Apriori is a great improvement in the
history of association rule mining, Apriori algorithm was
first proposed by Agrawal in [Agrawal and Srikant 1994].
The AIS is just a straightforward approach that requires
many passes over the database, generating many
candidate itemsets and storing counters of each candidate
while most of them turn out to be not frequent. Apriori is
more efficient during the candidate generation process for
two reasons; Apriori employs a different candidate’s
generation method and a new pruning technique. Apriori
algorithm still inherits the drawback of scanning the
whole data bases many times. Based on Apriori
algorithm, many new algorithms were designed with
some modifications or improvements. Generally there
were two approaches: one is to reduce the number of
passes over the whole database or replacing the whole
database with only part of it based on the current frequent
itemsets, another approach is to explore different kinds of
pruning techniques to make the number of candidate
itemsets much smaller. Apriori-TID and Apriori-Hybrid
[Agrawal and Srikant 1994], DHP [Park et al. 1995],
SON [Savesere et al. 1995] are modifications of the
Apriori algorithm. Most of the algorithms introduced
above are based on the Apriori algorithm and try to
improve the efficiency by making some modifications,
such as reducing the number of passes over the database;
reducing the size of the database to be scanned in every
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pass; pruning the candidates by different techniques and
using sampling technique. However there are two
bottlenecks of the Apriori algorithm. One is the complex
candidate generation process that uses most of the time,
space and memory. Another bottleneck is the multiple
scan of the database.
C. FP-Treed (Frequent Pattern Tree) Algorithm. To break
the two bottlenecks of Apriori series algorithms, some
works of association rule mining using tree structure have
been designed. FP-Tree [Han et al. 2000], frequent
pattern mining, is another milestone in the development
of association rule mining, which breaks the two
bottlenecks of the Apriori. The frequent itemsets are
generated with only two passes over the database and
without any candidate generation process. FP-Tree was
introduced by Han et al in [Han et al. 2000]. By avoiding
the candidate generation process and less passes over the
database, FP-Tree is an order of magnitude faster than the
Apriori algorithm. The frequent patterns generation
process includes two sub processes: constructing the FT-
Tree, and generating frequent patterns from the FP-Tree.
The efficiency of FP-Tree algorithm account for three
reasons, First the FP-Tree is a compressed representation
of the original database because only those frequent items
are used to construct the tree, other irrelevant information
are pruned. Also by ordering the items according to their
supports the overlapping parts appear only once with
different support count. Secondly this algorithm only
scans the database twice. The frequent patterns are
generated by the FP-growth procedure, constructing the
conditional FP-Tree which contain patterns with specified
suffix patterns, frequent patterns can be easily generated
as shown in above the example. Also the computation
cost decreased dramatically. Thirdly, FP-Tree uses a
divide and conquers method that considerably reduced
the size of the subsequent conditional FP-Tree; longer
frequent patterns are generated by adding a suffix to the
shorter frequent patterns. In [Han et al. 2000] [Han and
Pei 2000] there are examples to illustrate all the detail of
this mining process.
IV. CONCLUSION
Association rule mining is a popular area of research. There
are many real world applications are related to it. The
problem with current algorithms is the duplicate rule
generation. To overcome this problem we will proposed an
updated algorithm.
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