S. Bagga, N. Badal · 2015
Apriori algorithm mines the data from the large scale data warehouse using association rule mining. In this paper a new algorithm named as Dynamic Apriori (D-Apriori) algorithm is presented. The proposed D-Apriori algorithm incorporates the dynamism in classical Apriori for efficiently mining the frequent itemsets from a large scale database. With the help of experimental results, it is shown that the D-Apriori algorithm performs better than the existing Apriori algorithm with respect to execution time for the dynamic behavior of data itemset.
Cited by 0
SpringerReference · 2012
Cited by 1
Fangyuan Li (2202148), Xia Wang (36364), Zenglei Feng (18605463) et al. · Figshare · 2024
<div><p>The real-time monitoring on the risk status of the vehicle and its driver can provide the assistance for the early detection and blocking control of single-vehicle accidents. However, complex risk coupling relationship is one of the main features of single-vehicle accidents with high mortality rate. On the basis of investigating the coupling effect among multi-risk factors and establishing a safety management database throughout the life cycle of vehicles, single-vehicle driving risk network (SVDRN) with a three-level threshold was developed, and its topology features were analyzed to assessment the importance of nodes. To avoid the one-sidedness of single indicator, the multi-attribute comprehensive evaluation model was applied to measure the comprehensive effect of characteristic indicators for nodes importance. A algorithm for real-time monitoring of vehicle driving risk status was proposed to identify key risk chains. The result revealed that improper operation, speeding, loss of vehicle control and inefficient driver management were the sequence of top four risk factors in the comprehensive evaluation result of nodes importance (mean value = 0.185, SD = 0.119). There were minor differences of 0.017 in the node importance among environmental factors, among which non-standard road alignment had the larger value. The improper operation and non-standard road alignment were the highest combination correlation of factors affecting road safety, with the support of 51.81% and the confidence of 69.35%. This identification algorithm of key risk chains that combines node importance and its risk state threshold can effectively determine the high-frequency risk transmission paths and risk factors through multi-vehicle test, providing a basis for centralization management of transport enterprises.</p></div>
Cited by 0Open access
Rui Chang, Zhiyi Liu · 2011
In this study, it proposes a new optimization algorithm called APRIORI-IMPROVE based on the insufficient of Apriori. APRIORI-IMPROVE algorithm presents optimizations on 2-items generation, transactions compression and so on. APRIORI-IMPROVE uses hash structure to generate L2, uses an efficient horizontal data representation and optimized strategy of storage to save time and space. The performance study shows that APRIORI-IMPROVE is much faster than Apriori.
Cited by 27
Andrew Kusiak · 2012
Cited by 0
Dongling Bai · 2013
Apriori algorithm is a classical algorithm in data mining association rules.However the algorithm scans the database so many times causes that it runs too slowly.A new Apriori algorithm is proposed after Apriori is analyzed.The main idea of the new Apriori algorithm is that transaction database is converted to corresponding 0-1 matrix,whose each vector and subsequent vector does inner product operation to get support.Comparing with the given minsupport,it deletes the rows and columns if they are less than the minsupport to reducing the size of the rating matrix.It makes the computing more efficient paper It runs slowly as it scans the database frequently.The improved algorithm only needs to scan the database once so that it runs quickly.The experiment results prove that it is efficient and feasible for the improved algorithm.
Cited by 1
J. Z. Gu, Bao-jin Wang, Fengyu Zhang et al. · Communications in computer and information science · 2011
Cited by 11
Majumdar, Partha · Zenodo (CERN European Organization for Nuclear Research) · 2024
“Apriori Algorithm for Engineers” provides a comprehensive introduction to association rule mining and its applications in recommendation systems. This book explores the Apriori Algorithm’s principles, practical implementations, and its role in uncovering patterns within large datasets. With detailed examples, Python code, and a Farmer’s Market case study, it bridges theory and real-world practice. Designed for engineers, analysts, and students, it equips readers with the tools to create data-driven solutions across various industries, making it an indispensable resource in the age of big data.
Cited by 0Open access
Bai Si-xue, Xinxi Dai · 2007
Apriori algorithm is the most classical algorithm in association rule mining, but it has two fatal deficiencies: more scans for the databases and generates a great deal of candidate itemsets. To solve these two deficiencies of Apriori algorithm, an efficient and fast algorithm based on Pattern Matrix (named P_Matrix algorithm) is proposed, which solves these two problems: scanning for the databases is cut down to one time and not generating the candidate itemsets but generating the frequent itemsets directly. It greatly reduces the temporal complexity and spatial complexity of the algorithm and highly promotes the efficiency of Aprior algorithm. Key words: data mining; association rule; Apriori algorithm; P_Matrix algorithm
Cited by 7
Yahya Zakur, Laith Flaih · E3S Web of Conferences · 2023
Data mining has the potential to empower healthcare organizations by allowing them to analyze various aspects of patient information and discover connections between seemingly unrelated data. By harnessing advanced data analysis techniques, healthcare providers can identify trends in patients' medical conditions and behaviours. The Apriori algorithm is used for mining frequent item sets and devising association rules from a transactional database. The parameters “support” and “confidence” are used. Support refers to items’ frequency of occurrence; confidence is a conditional probability, while Apriori-Hybrid. Apriori-Hybrid is the combination of algorithms Apriori and Apriori-TID, which can classify large itemsets and can improve the accuracy of classification and it can also shed light on the basic mechanism. In this research, a comparison was made between the two algorithms in terms of capabilities, strengths, areas of use, and suggestions about the nature of using each algorithm.
Cited by 7Open access
HASMORO, ANDRI · Unika Repositor (Unika) · 2013
Promotions and will further facilitate the placement items in the new offer \nand prodak shall preclude people will have difficulty in finding items or \ninformation. \nBut it still hurts a lot in making placement or promote items are not \npurchased frequently. So the seller is difficult to find a way out of this \nproblem. \nThis application uses apriori algorthm calculation that can \ndetermine the item that often arises is bounded with minimal support. So \nthe results of frequency calculations can facilitate the placement of a seller \nitem and offering new item. In order to facilitate the buyers in search item. \nThe project was created using Apriori Algorithm with HTML5 \nprogramming language and Jquery programming language. With an \nattractive interface and easy process then this application will make it \neasier to use.
Cited by 0Open access
Roja' Putri Cintani, Desti Fitriati · Jurnal Riset Informatika · 2025
PT JR Pangan Semesta is a company that produces fast food in the form of Donuts and Sweet Bread under the Deroti brand. The sales and promotion methods that have been carried out have weaknesses because the company has difficulty ensuring the right amount of bread production, so there is often excess or lack of stock. In addition, the promotional strategy used has not included the concept of bundling, so the maximum promotional potential has not been fully explored. To overcome these problems, the use of data mining methods is proposed, one of which is the Apriori Association Rule algorithm. Apriori algorithm is used to find consistent sales patterns and find strong product relationships by analyzing sales transaction data. In this study, sales patterns were analyzed at PT JR Pangan Semesta with a minimum support value of 16% and a minimum confidence value of 60%. The analysis results show that there are three products that are often purchased together by consumers, namely Fried Bread, Deroti Donuts, and Eco Donuts. The three products form one valid association rule, so that the rule can be used as a reference for developing efficient production methods for bread and donuts and implementing sales strategies in the form of bundling products to maximize profits.
Cited by 0Open access
Markus Hegland · Lecture notes series, Institute For Mathematical Sciences · 2007
Cited by 114
Fangyuan Li (2202148), Xia Wang (36364), Zenglei Feng (18605463) et al. · Figshare · 2024
<div><p>The real-time monitoring on the risk status of the vehicle and its driver can provide the assistance for the early detection and blocking control of single-vehicle accidents. However, complex risk coupling relationship is one of the main features of single-vehicle accidents with high mortality rate. On the basis of investigating the coupling effect among multi-risk factors and establishing a safety management database throughout the life cycle of vehicles, single-vehicle driving risk network (SVDRN) with a three-level threshold was developed, and its topology features were analyzed to assessment the importance of nodes. To avoid the one-sidedness of single indicator, the multi-attribute comprehensive evaluation model was applied to measure the comprehensive effect of characteristic indicators for nodes importance. A algorithm for real-time monitoring of vehicle driving risk status was proposed to identify key risk chains. The result revealed that improper operation, speeding, loss of vehicle control and inefficient driver management were the sequence of top four risk factors in the comprehensive evaluation result of nodes importance (mean value = 0.185, SD = 0.119). There were minor differences of 0.017 in the node importance among environmental factors, among which non-standard road alignment had the larger value. The improper operation and non-standard road alignment were the highest combination correlation of factors affecting road safety, with the support of 51.81% and the confidence of 69.35%. This identification algorithm of key risk chains that combines node importance and its risk state threshold can effectively determine the high-frequency risk transmission paths and risk factors through multi-vehicle test, providing a basis for centralization management of transport enterprises.</p></div>
Cited by 0Open access
Akshay Palekar, Komal Narwekar, Pooja Manjeshwar et al. · International Journal of Engineering Trends and Technology · 2015
Cited by 2
Xin Luo (428648), Jijia Sun (13202211), Hong Pan (8686) et al. · Figshare · 2023
<div><p>In recent years, the prevalence of T2DM has been increasing annually, in particular, the personal and socioeconomic burden caused by multiple complications has become increasingly serious. This study aimed to screen out the high-risk complication combination of T2DM through various data mining methods, establish and evaluate a risk prediction model of the complication combination in patients with T2DM. Questionnaire surveys, physical examinations, and biochemical tests were conducted on 4,937 patients with T2DM, and 810 cases of sample data with complications were retained. The high-risk complication combination was screened by association rules based on the Apriori algorithm. Risk factors were screened using the LASSO regression model, random forest model, and support vector machine. A risk prediction model was established using logistic regression analysis, and a dynamic nomogram was constructed. Receiver operating characteristic (ROC) curves, harrell’s concordance index (C-Index), calibration curves, decision curve analysis (DCA), and internal validation were used to evaluate the differentiation, calibration, and clinical applicability of the models. This study found that patients with T2DM had a high-risk combination of lower extremity vasculopathy, diabetic foot, and diabetic retinopathy. Based on this, body mass index, diastolic blood pressure, total cholesterol, triglyceride, 2-hour postprandial blood glucose and blood urea nitrogen levels were screened and used for the modeling analysis. The area under the ROC curves of the internal and external validations were 0.768 (95% CI, 0.744−0.792) and 0.745 (95% CI, 0.669−0.820), respectively, and the C-index and AUC value were consistent. The calibration plots showed good calibration, and the risk threshold for DCA was 30–54%. In this study, we developed and evaluated a predictive model for the development of a high-risk complication combination while uncovering the pattern of complications in patients with T2DM. This model has a practical guiding effect on the health management of patients with T2DM in community settings.</p></div>
Cited by 0Open access
Changsheng Zhang, Zhongyue Li, Dongsong Zheng · 2009
In this paper, we studied the Apriori Algorithm and its performance, which is used to mine association rules. The algorithm for reducing the size of the candidate-itemsets is optimized. Meanwhile, the performance of algorithm is improved to reduce I/O spending by cutting down unnecessary transaction records.
Cited by 10
Ch. Bhavani, Pappula Madhavi · International Journal of Computer Trends and Technology · 2015
Cited by 1
Ou Pan · Advances in intelligent systems and computing · 2021
Cited by 1
Hong-Goo Kang, Sangjin Lee · ACM Inroads · 2020
research-article Share on Automated analysis with Apriori algorithm: deriving guidance for working-level cybersecurity curricula Authors: Honggoo Kang Korea University School of Cybersecurity/Defense Security Support Command Korea University School of Cybersecurity/Defense Security Support CommandView Profile , Sangjin Lee Korea University School of Cybersecurity Korea University School of CybersecurityView Profile Authors Info & Claims ACM InroadsVolume 11Issue 4December 2020 pp 20–26https://doi.org/10.1145/3410471Online:12 November 2020Publication History 0citation62DownloadsMetricsTotal Citations0Total Downloads62Last 12 Months20Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
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