Data Mining Association Rules Research Articles

E-Commerce Data Mining Analysis based on User Preferences and Assiciation Rules

Improving the sales of e-commerce platforms is the primary goal of this paper. This paper studies the data of e-commerce product recommendations from the perspective of user preference and association rules. The characteristics of positive and reverse association rules in data mining are analyzed. Then, a multi-dimension association rule calculation method is proposed. Create a data attribute unit set. By analyzing each attribute's weighted coefficient and similarity, the attribute confidence degree is obtained, and the data is preprocessed. An example is given to verify the effectiveness of the proposed method. The recommendation engine based on user preferences and association rules significantly improves the accuracy, recall rate and prediction coverage of e-commerce recommendation systems.

Apriori Algorithm Application for Consumer Purchase Patterns Analysis

The Apriori algorithm is a data mining association rule algorithm for finding relationship patterns between one or more items in a dataset. Apriori algorithm is often used in transaction data analysis or market basket analysis. Apriori algorithm is used to find out consumer purchase patterns in e-commerce systems and provide product recommendations to consumer by extaracting associations or events from transactional data. This study is purposed for deeply analyze the steps, performance of Apriori algorithm, and give relevant an example of case study to better explain the steps of Apriori algorithm application, as well as the results achieved.

Data Mining Technology in Teaching Evaluation of Colleges and Universities

Data Mining refers to the large amount of data from the database through algorithmic search reveals implicit, previously unknown and potentially valuable process information[1]. Currently, many areas during the application of data mining. Data mining association rules is one of the most important and most mature technology research methods, association rule mining can find the hidden link between the transaction and meaningful rules. The purpose of this study is to evaluate data mining techniques combined with teaching, to extract useful information from a large number of evaluation data hiding, thereby providing a basis for decision support educational administration department, improve teaching quality.

Analisis Association Rule Untuk Memprediksi Kelulusuan Mahasiswa F-Kip Universitas Graha Nusantara Padangsidimpuan

University administrators need graduation predictions to determine initial steps to avoid dropout. The length of a student's study period is determined by various factors. Therefore, it is important to know which students may not graduate on time. Data mining techniques can be used to explore new insights to predict student graduation. By using the association rule technique we can obtain information from large data such as data from universities. The aim of this research is to determine the pattern of study duration for Graha Nusantara University F-KIP students. by using the association rule data mining method and comparing a priori algorithms and hash-based algorithms. The data used is Graha Nusantara University F-KIP master data which is processed using association rule data mining techniques with a priori algorithms and hash-based algorithms with minimum support of 1% and minimum confidence of 1%. The results of data processing with the a priori algorithm are the same as the results of data processing with the hash-based algorithm, namely 49 2-itemset combinations. The pattern that was formed included 7.5% of graduates from the mathematics department studying for more than 5 years with a confidence value of 38.5%.

Reform and Innovation of Dance Education in Colleges and Universities in the Internet Era

Abstract This paper firstly proposes the reform strategy and innovation direction of college dance education based on the Internet background of the era and adopts Webwork+Spring+Hibernate to build the overall architecture of the dance education system and designs the functional modules of the dance education system. Then, establish the dance teaching model based on association rules, use the association rule data mining algorithm to mine specific laws to improve the prediction model, and measure the standard of association rules by T test. Finally, the dance education system is tested for operation, compatibility, response time, and application analysis. The results show that the average score of students’ final exam before applying the dance teaching system is 75.23, and the average score of students’ final exam after applying it is 86.12, which is an improvement of 10.89 points, which shows that the dance education system constructed in this paper is able to improve the student’s academic performance, and promote the reform of dance education in colleges and universities effectively, and provide a new direction for the development of dance education in colleges and universities.

Data Mining Analysis for Improving Decision-Making in Computer Management Information Systems

In this paper a decision tree-based data mining procedure for information systems is proposed to enhance the accuracy and efficiency of data mining. An enhanced C4.5 decision tree method based on cosine similarity is suggested to evaluate the information gain rate of characteristics and the information entropy of their values. When the information entropy variance among any two values for attributes is within the threshold range, the cosine similarity of the merging attribute values is determined, and the information gain rate of the attributes is recalculated. Large-scale data sets that conventional data processing methods are unable to handle successfully have given rise to the area of data mining. The prime objective is to look into how data mining technology is used in computer management information systems. The benefits of data mining technologies in computer management information systems are examined from a variety of angles in this study. In order to analyze and comprehend huge data sets and to derive knowledge that can be utilized to enhance the decision-making process in computer management information systems, the suggested solution makes use of a number of data mining techniques, including Clustering, Classification, and Association Rule Mining. The experimental analysis indicates that the time required by the proposed method to construct a decision tree is less than the time required by the GBDT, P-GBDT method and the C5.0 decision tree Hyperion image forest type fine classification method. The minimum time is not more than 15 seconds when compared with the minimum time saving of the other two methods. The time required by the C5.0 decision tree Hyperion image forest type fine classification method is always the greatest in comparison with the minimum time saving of the C5.0 decision tree. The classification accuracy of the proposed method for various datasets exceeds 95 percent, and the data mining efficacy is high. This method enhances the precision and efficacy of data mining in order to uncover valuable information concealed behind a large volume of data and maximize its value.

A big data association rule mining based approach for energy building behaviour analysis in an IoT environment

The enormous amount of data generated by sensors and other data sources in modern grid management systems requires new infrastructures, such as IoT (Internet of Things) and Big Data architectures. This, in combination with Data Mining techniques, allows the management and processing of all these heterogeneous massive data in order to discover new insights that can help to reduce the energy consumption of the building. In this paper, we describe a developed methodology for an Internet of Things (IoT) system based on a robust big data architecture. This innovative approach, combined with the power of Spark algorithms, has been proven to uncover rules representing hidden connections and patterns in the data extracted from a building in Bucharest. These uncovered patterns were essential for improving the building’s energy efficiency.

SMART AND INTELLIGENT PRODUCTION STRATEGY FOR THE FLOWER MARKET USING DATA MINING KNOWLEDGE-BASED DECISION

The agriculture industry has been an enormous economic pillar in the production and consumption market value chain. The agriculture industry resets flower production factors with the agricultural technology revolution. The fastest technology provides innovative and intelligent decision-making strategies in seasonal cut flowers to increase production. This study briefs out existing farming practices, chain activity and farming technology’s significant impacts on the agriculture field and garden industry. Authors try to investigate cut flower production status and analyze production values to design innovative and intelligent strategies, especially for seasonal flower production. The study employs a flower dataset; hence, it applies floral parameter inputs and data mining association rules to create an output of the flower production category, which fits appropriately to evaluate flower market production value in a particular season. The article's result reveals that the proposed flower production strategy provides efficient and intelligent guidelines to increase flower production according to market demand. This study suggests an intelligent and friendly production strategy for gardeners that indicates the flower market gets continuous and quality production to meet consumers’ immediate market demand.

Demand-Oriented Review of a Dynamic Energy-Loss Monitoring System for Primary School Buildings through Micro-Environmental Data Monitoring and Occupant Behavior Analysis

The utilization of primary school buildings is multifaceted, primarily due to the high occupancy density, varying thermal preferences among occupants, diverse indoor activities (such as walking, sports, and conversation), and a constant flow of individuals entering and exiting the building. This results in the frequent opening and closing of external windows and doors and fluctuations in internal heat gain. Consequently, frequent interactions between the indoor and outdoor microenvironments lead to energy losses. This study conducts a comprehensive literature review on building energy loss stemming from occupant behavior and the interactions between indoor and outdoor microenvironments. Furthermore, it proposes a dynamic real-time monitoring system based on a foundation of computer data capture and a visualization platform for building energy loss. The research methods include data crawling, data association rule mining, and data association analysis. The research findings yield a universally applicable and informative building energy-saving design system based on extensive data analysis. Additionally, the system presents information on occupants’ behavior and the microclimate data of indoor and outdoor environments on a computer screen, facilitating human–machine communication and enabling timely adjustments to be made, thus facilitating the construction of design strategies for new buildings and operation and maintenance strategies for existing buildings.

IMPLEMENTASI ALGORITMA APRIORI DALAM PEMODELAN POLA PEMBELIAN PRODUK PADA EDUKITS BTC

In competition in the business world, especially the retail industry, requires developers to find an appropriate strategy in order to increase sales of school equipment, one way is to know the sales pattern of school equipment so that we can apply the right steps to provide more facilities, in order to increase selling power. A priori algorithm is a type of association rule in data mining. A priori is a very famous algorithm for finding high frequency patterns. A high frequency pattern is an item pattern in the database that has frequency or support above a certain threshold which is called the minimum support term. Using an a priori algorithm can help develop a marketing strategy. The results showed that with a minimum support of 30%, there were 4 combination patterns of one item set that achieved minimum support, with the highest support for Stationery Office of 72.6%, for a combination of 2 itemsets there were 4 combination patterns with the highest support (School Stationery, Stationery Office) namely 54.8%, for a combination of 3 itemsets there was 1 combination pattern with the highest support (Art & Craft, School Stationery, Office Stationery) namely 32.9%.

Classification Technique and its Combination with Clustering and Association Rule Mining in Educational Data Mining — A survey

Educational data mining (EDM) is the application of data mining in the educational field. EDM is used to classify, analyze, and predict the students’ academic performance, and students’ dropout rate, as well as instructors’performance in order to improve teaching–learning process. This review article discusses the detailed analysis of 142 research articles from publication year 2010-2020 downloaded from the research databases such as IEEE, Springer, ACM, and Elsevier. Also this review article contains the current happenings related to EDM in year 2021 and 2022. In this review article, the use of classification techniques and classification techniques along with other data mining techniques such as clustering algorithm, association rule algorithms, regression techniques and ensemble techniques in EDM are presented thoroughly. The comparative study is considered for Classification Techniques; Classification and Clustering Technique; Classification ans Association Rule Mining; Classification, Clustering and Association rule mining; Classification, Regression, and Clustering; and Classification, and Ensemble. Analysis in terms of Yearwise Number of Research Articles employing Classification Techniquein EDM; Classification with other Data Mining Technique used in EDM; classifier as per Weka Tool; Classification Techniques; Clustering Techniques; Association Rule Techniques; Selecting the best Classification Technique; Classification performance metric; software used in EDM; Sampling Period; size of dataset; and data mining tools are illustrated.From review of 142 research articles, it is noted that classification techniques are mostly used technique for analyzing students’ performance in EDM. Also classification technique along with clustering techniques are applied to predict the performance of students. It is found that Naïve Bays, Random Forest, Support vector machine and J48 are mostly considered classification techniques while in classification along with clustering techniques, K-means clustering algorithm is used with classification algorithms. The classification algorithms such as Naïve Bays, Random Forest and Support Vector Machine are noted to be the best classification algorithms after comparing various classification algorithms based on various performance parameters. Among various performance parameters, the parameters accuracy, precision, recall, f-measures and k-fold value found to be used by most of the research articles. Programming languages used to build the model in EDM for analyzing the students’ dataset from educational setting, are Java, R and Python programming languages while data mining tools considered to evaluate the performance of classification or clustering or association rule algorithms are Weka, and RapidMiner. Classification algorithms under the classifiers as per Weka tool such as Tree, Bays, Function and PMML classifier are applied in most of the research articles.In addition to comparative analysis and analysis based on various factors, research gaps are also identified and mentioned the same in this article. Future direction for researcher working in EDM related to building the model on the dataset obtained from educational setting to predict students’ performance are discussed so that work in EDM can be carried out to improve the teaching–learning process.

An Improved Algorithm and Implementation of Data Mining for Intelligent Manufacturing Association Rules Based on Pattern Recognition

With the deepening of the research of artificial intelligence theory, the research of pattern recognition has made further development, and the applicable fields have also been continuously expanded. Pattern recognition can be used in text and speech recognition, remote sensing nuclear medicine diagnosis. This article optimizes and analyzes intelligent manufacturing based on pattern recognition, and uses improved association rule data mining algorithms to improve it from many aspects, Association rules are not limited by the number of dependent variables and can discover associations between data in large databases. Data analysis is carried out based on factors in different production stages of intelligent manufacturing to demonstrate the feasibility of the improved algorithm of association rule data mining. The experimental simulation results show that the application of the improved pattern recognition model in this research has made the estimated value of the remanufactured product remanufactured to a maximum of 4.05, and the production effect has also been greatly improved, the floating range is between 6.13 and 7.90, and the overall evaluation can reach 9.06. The experimental results show that the effect of the improved pattern recognition model is greatly improved.

Exploring how feedback through questioning may influence reflective thinking skills based on association rules mining technique

Reflective thinking skills are essential to empower students with 21st century skills. Nevertheless, lack of feedback support has caused students to struggle in developing their reflective thinking skills. Despite the abundance of literature related to feedback practices, the notion of how feedback should be tailored and creatively delivered requires further research. Hence, this study aims to ascertain about how the provision of specific feedback through questioning approach can trigger specific reflective thinking skills using innovative analytical analysis technique, that is, association rules mining. An explorative case study qualitative design was employed and the study was conducted for 14 weeks. Six case-based activities that reflect real-world scenarios were created for students to critically discuss and solve in the blogging environment. Association rules data mining technique was utilised on qualitative data of reflections on both course and individual blogs, to model how feedback through questioning may influence the direction of reflective thinking skills. The findings revealed 16 important association rules and from that only Limited Focal, Open Focal, Brainstorming and Application types of feedback were discovered to lead to important reflective thinking skills development. It was also found that students’ reflective discussion cannot simply move beyond reportive/descriptive thinking and decision-making, despite being pressed by higher level feedback. The overall findings of this study also have implications towards parties like students, instructors, and syllabus and materials designers in terms of how to creatively develop and teach reflective thinking skills via the feedback through questioning approach in educational blogging environments.

An Adaptive Privacy Preserving Framework for Distributed Association Rule Mining in Healthcare Databases

It is crucial, while using healthcare data, to assess the advantages of data privacy against the possible drawbacks. Data from several sources must be combined for use in many data mining applications. The medical practitioner may use the results of association rule mining performed on this aggregated data to better personalize patient care and implement preventive measures. Historically, numerous heuristics (e.g., greedy search) and metaheuristics-based techniques (e.g., evolutionary algorithm) have been created for the positive association rule in privacy preserving data mining (PPDM). When it comes to connecting seemingly unrelated diseases and drugs, negative association rules may be more informative than their positive counterparts. It is well-known that during negative association rules mining, a large number of uninteresting rules are formed, making this a difficult problem to tackle. In this research, we offer an adaptive method for negative association rule mining in vertically partitioned healthcare datasets that respects users’ privacy. The applied approach dynamically determines the transactions to be interrupted for information hiding, as opposed to predefining them. This study introduces a novel method for addressing the problem of negative association rules in healthcare data mining, one that is based on the Tabu-genetic optimization paradigm. Tabu search is advantageous since it removes a huge number of unnecessary rules and item sets. Experiments using benchmark healthcare datasets prove that the discussed scheme outperforms state-of-the-art solutions in terms of decreasing side effects and data distortions, as measured by the indicator of hiding failure.

Association Rule Mining in Transactional Data: Challenges and Opportunities

Association Rule Mining in Transactional Data: Challenges and Opportunities

Regional Geological Disasters Emergency Management System Monitored by Big Data Platform

In order to deal with the hazards caused by geological disasters in time, an emergency management system is proposed based on association rule data mining. With the support of a big data platform, a regional geological disaster emergency management system is built based on monitoring data. In the result analysis, the association rule algorithm demonstrates high computing power in the test, which can filter the data with strong association rules. In addition, the big data platform can allow data visualization, which has good data storage capacity and disaster early warning capacity. In the simulation test of the emergency management system, it was found that the system is feasible in theory. When it is applied to the actual disaster emergency management, it wasfound that, in the face of geological disasters, the processing speed of relevant departments increased by 59.4%, and the allocation of personnel and materials wasmore reasonable. The above results show that the big data platform monitoring data can improve the regional geological disasters emergency management capacity and ensure the safety of people’s lives and property.

Assessing the tendency of judging bias in student competition: a data mining approach

PurposeJudging bias is ironically an inherent risk in every competition, which might threaten the fairness and legitimacy of the competition. The patriotism effect represents one source of judging bias as the judge favors contestants who share the same sentiments, such as the nationalistic, racial, or cultural aspects. This study attempts to explore this type of judging bias in a university student competition. In addition, this study tries to expand the literature on judging bias by proposing the term universitarian bias as the judge is suspected to give a higher score to contestants from the same university.Design/methodology/approachThe association rule of data mining is used to accomplish the objective of the study. To demonstrate the applicability of the method, the data set from the annual national university student competition in Indonesia is exploited.FindingsThe result strongly discovers that the universitarian bias is likely to be present. Some recommendations are also provided in order to minimize the bias that might happen again in the future.Practical implicationsAs the implication of the presence of the universitarian bias, the committee should remove all the university features attributed to the participants. This endeavor is expected to minimize the universitarian bias that might happen.Originality/valueThis research is claimed to be the first attempt in implementing the data mining technique in the field of judging bias.

Neutrosophic Fuzzy Association Rule Generation-Based Big Data Mining Analysis Algorithm

As a very common and classic big data (BD) mining algorithm, the association rule data mining (DM) algorithm is often used to determine the internal correlation between different items and set a certain threshold to determine the size of the correlation. However, the traditional association rule algorithm is more suitable for establishing Boolean association rules between different items of different types of data, and hardening the sharp boundaries of the data causes the performance of the association rules to decrease. In order to overcome this shortcoming of classic DM, this article introduces association rules, support and confidence, the Apriori algorithm and fuzzy association rules based on the neutrosophic fuzzy association rule (NFAR). This paper is based on the data set of the supermarket purchase goods database, by drawing a radar chart to describe the correlation between different goods and different item sets support, and confidence calculation based on association rules support. Finally, the association rules are generated. Compared to the results produced by NFAR and ordinary association rules, the accuracy of the NFAR association rules algorithm in small data sets is 88.48%, while the accuracy of traditional association rules algorithm is only 80.87%, nearly 8 percentage points higher. On large data sets, the prediction accuracy of the neutral fuzzy association rules algorithm is 95.68%, while that of the traditional method is only 89.63%. Therefore, the NFAR algorithm can improve the accuracy and effectiveness of DM. This algorithm has great application prospects and development space in big DM and analysis.

Comprehensive Evaluation of Government Economic Management Performance Based on Multidimensional Data Mining in Fuzzy Comprehensive Environment.

Economic, political, social, and military activities all fall under the umbrella of government duties. The modification and reinterpretation of economic functions are the primary focus of the innovation in government administration style under the wave of economic globalisation. The effectiveness of the government's economic administration determines the general level of public administration at the federal level. An essential issue of national public administration that has a significant impact on the political growth of many nations is the performance evaluation of government economic administration. The people are the subject of government economic administration performance evaluation, and the people adjudicate the outcomes. An essential method of assessing the productivity of various departments is through performance review. The nation is currently in the process of switching from its long-standing planned economic system to a market economic system. The internal and external environments of governmental organisations are continually changing in addition to the strong trend of economic globalisation. Determining and advancing the national government's economic administration model is so crucial. The article offers a multidimensional data mining-based optimum design scheme for the thorough assessment of government economic administration performance. The fuzzy comprehensive evaluation rule is an effective method for quantifying the qualitative indicators when the quantitative indicators in the evaluation index system are difficult to measure. It is relatively simple, reasonable, and simple to operate in practise, which is conducive to the thorough and scientific performance evaluation of the government economic administration's science and technology administration functions. Following an assessment of the system's performance using association rule data mining technologies, a simulation test analysis is completed. The accuracy of the proposed arithmetic, which is 8.26% higher than the conventional arithmetic, is demonstrated by simulation results. The development of an evaluation model that incorporates both subjective and objective criteria, as well as the thorough assessment of the effectiveness of government economic administration based on data mining technologies, has excellent application prospects and practical value.

Comparing Decision Trees and Association Rules for Stock Market Expectations in BIST100 and BIST30

With the increased financial fragility, methods have been needed to predict financial data effectively. In this study, two leading data mining technologies, classification analysis and association rule mining, are implemented for modeling potentially successful and risky stocks on the BIST 30 index and BIST 100 Index based on the key variables of index name, index value, and stock price. Classification and Regression Tree (CART) is used for classification, and Apriori is applied for association analysis. The study data set covered monthly closing values during 2013-2019. The Apriori algorithm also obtained almost all of the classification rules generated with the CART algorithm. Validated by two promising data mining techniques, proposed rules guide decision-makers in their investment decisions. By providing early warning signals of risky stocks, these rules can be used to minimize risk levels and protect decision-makers from making risky decisions.