Association Rule Mining Research Articles

Identifying attribute associations in fatal speeding crashes using latent class clustering and association rule mining

Identifying attribute associations in fatal speeding crashes using latent class clustering and association rule mining

Investigation of the risk influential factors of maritime accidents: A novel topology and robustness analytical framework

This study aims to develop a novel and fully data-driven approach to analyse the maritime accidents risk influential factors (RIFs) by integrating Association Rule Mining (ARM) and Complex Network (CN) modelling. Firstly, a comprehensive dataset comprising 21,206 maritime accident records from Marine Accident Investigation Branch and Transportation Safety Board is collected and processed to serve as the foundational data source supporting the development of the new approach. Secondly, a novel Combined Association Rule Mining method is proposed to extract the interconnections among RIFs, with the mined results mapped into a CN framework. Finally, two importance ranking algorithms, namely the PageRank-Information-Entropy algorithm and edge betweenness centrality, are applied to identify the key RIFs and their information transmission paths. By simulating deliberate and random attacks within this network, a robustness analysis is conducted to further explore the evolution of RIFs. The findings reveal that ship-related factors demonstrate greater centrality and connectivity, exerting a more substantial influence on information propagation within the network structure. The robustness analysis illustrates that strategic node and edge removals are effective in preventing risk propagation. It therefore makes contributions to the development of a theoretical basis for stakeholders to develop cost-effective preventive measures against specific RIFs, ultimately enhancing maritime safety.

Utilizing association rule mining for enhancing sales performance in web-based dashboard application

Utilizing association rule mining for enhancing sales performance in web-based dashboard application

Educational Data Mining: A Foundational Overview

Educational data mining (EDM) is a novel scientific area that focuses on developing and applying methods to analyze datasets generated within educational settings. This paper outlines the evolution, significance, and applications of EDM. With the increasing popularity of e-learning in web-based educational systems, EDM has expanded to include a variety of analytical methods and data sources. Some key methodologies addressed include classification, regression analysis, clustering techniques, association rule mining, and Natural Language Processing, among others. Additionally, this paper looks at how EDM can facilitate data-driven decision-making among other areas such as curriculum development and customization of learners’ experiences. It also touches on issues related to the challenges of the scientific field. Finally, some projections about EDM’s future trends are made, especially concerning its integration into AI technologies and development trends like augmented reality or virtual reality, which imply greater possibilities for changes than any other series witnessed before within this sphere.

A Systematic Literature Review on The Role of LSTM Networks in Capturing Temporal Dependencies in Data Mining Algorithms

Data mining plays a crucial role in extracting meaningful insights from large and complex data sets, with broad applications in sectors like finance, health care, and market analysis. Traditional techniques—such as classification, clustering, association rule mining, regression, and anomaly detection—are effective for analyzing structured data but struggle with sequential data due to the challenges of modeling temporal dependencies. Long Short Term Memory (LSTM) networks, a specialized form of Recurrent Neural Networks (RNNs), provide a solution to these challenges. By incorporating memory cells and gating mechanisms, LSTM effectively manage long-term dependencies and address issues like vanishing and exploding gradients. This paper reviews the impact of LSTM networks on data mining, analyzing over 60 key publications. By synthesizing concepts and recent advancements, the review underscores how LSTMs enhance the ability of data mining algorithms to capture and predict temporal patterns, reflecting current research trends and innovations

Fuzzy Association Rule Mining for Personalized Chinese Language and Literature Teaching from Higher Education

Due to rapid information technology growth, teaching Chinese in higher education has changed, and Chinese literary majors have vigorously evolved. The key teaching difficulties are scalability, individualized teaching, and a lack of resources and methodologies. Research shows individualized education improves topic comprehension, cultural engagement, and learner interest. Fuzzy association rule mining uses fuzzy linguistic values and membership functions to provide more realistic results. Hence, an algorithm, EF-PCL2T, has been proposed to improve personalized Chinese language and literature teaching (PCL2T) using enhanced fuzzy (EF) Apriori association rule mining integrated with the genetic algorithm. Fuzzy Apriori association rule mining identified frequent itemsets with relevant learning patterns and produced applicable association rules from datasets with fuzzy or unclear information, capturing fluctuating itemset importance and providing a flexible representation of relationships to determine student preferences. From fuzzy-related data, a genetic algorithm optimizes skill sets and creates individualized lesson plans considering each student’s competency and preferences for adjusting to personalized teaching tactics. Testing shows that fuzzy enhancement association rule mining for the PCL2T model improves student retention, PET (personalized teaching efficiency), minimal support and confidence update with fuzzy rules, and student involvement compared to other state-of-the-art methods. Students agree that tailored Chinese language and literary instruction is possible. The improvement results show fuzzy rules with minimum confidence levels of 50% to 100%, highly correlated in this model, student retention ratio of 96%, improved assessment grade of various language skills by 40 marks, PTE analysis of 93%, and student involvement ratio of 97%.

Comorbidity Patterns in Chronic Obstructive Pulmonary Disease and Their Associations with Service Utilization

Background The prevalence of combinations of comorbidities and their associations with inpatient service utilization and readmission among patients with chronic obstructive pulmonary disease (COPD) have not been extensively examined. To address this gap in knowledge, an observational prospective study was conducted using retrospective data. Aims To identify patterns of comorbidities linked to length of hospital stay, daily expenses, and one-year readmission. Methods The 30 most common comorbidities were identified in patients with secondary diagnoses using the association rule mining (ARM) method. Regression models were used to examine the relationships between combinations of comorbidities and service utilization, with adjustments for covariates. Results The five most prevalent comorbidities were pulmonary heart disease (40.99%), ischemic heart disease (38.97%), heart failure (36.77%), hypertension (34.11%), and respiratory disorders (19.12%). Most combinations of comorbidities identified by ARM showed significant associations with an extended length of stay (>13 days), increased daily expenses (>930 CNY), and reduced readmission rates. Among these combinations, glycoprotein metabolism disorder had the strongest association with prolonged length of stay (adjusted odds ratio [aOR]): 1.89, 95% confidence interval [CI]: 1.82–1.95). Conversely, the combination of other brain diseases and respiratory failure was linked to higher daily expenses (aOR: 11.34, 95% CI: 10.58–12.15), and the presence of pulmonary heart disease was associated with elevated one-year readmission rates (aOR: 1.41, 95% CI: 1.37–1.46). Conclusion Common combinations of comorbidities among inpatients with COPD were identified from an extensive collection of discharge medical records. Furthermore, the associations between comorbidities, inpatient service usage, and readmission rates were determined.

Exploring patterns in older pedestrian involved crashes during nighttime

Nighttime crashes involving older pedestrians pose a significant safety concern due to their age-related vulnerabilities such as reduced vision and slower reaction times. This study analyzes crash data from Texas for six years (2017–2022) using Association Rules Mining (ARM) to identify patterns and associations affecting crash severity for older pedestrians aged 65–74 years and those over 74 years under varying lighting conditions. The findings reveal that high-speed limits and complex road environments significantly increase the risk of fatal or severe injuries for both age groups, particularly under inadequate lighting. Additionally, demographic factors, adverse weather conditions, and specific road features further influence crash outcomes. These insights highlight the need for interventions, including lower speed limits, enhanced street lighting, and the implementation of advanced technologies such as modern pedestrian detection systems, sensor technology, pedestrian bags, accessible pedestrian signals, to improve the safety of older pedestrians. Policymakers should leverage these insights to formulate strategies that improve road safety for older pedestrians, addressing their unique vulnerabilities in various nighttime conditions.

Identifying Factors of Dynamic Positioning Incidents through Association Rule Mining

Accidents in the offshore industry can have severe repercussions for people, cargo, vessels, and the environment, making maritime safety a crucial concern. Dynamic positioning incidents, particularly those involving loss of position, represent a significant risk. This study employs association rule mining to analyze DP incident data, leveraging its strength in discovering robust associations. Using the Apriori algorithm, the analysis identifies strong association rules for loss of position (drift-off, drive-off) and loss of redundancy situations. The findings reveal event-related variables and potential causal relationships, providing insights and guidance for reducing the risk and occurrence of future DP incidents through stringent and targeted safety measures.

Property Valuation in Latvia and Brazil: A Multifaceted Approach Integrating Algorithm, Geographic Information System, Fuzzy Logic, and Civil Engineering Insights

This study aimed to predict residential apartment prices in Latvia and Brazil using algorithms from machine learning, fuzzy logic, and civil engineering principles, with a focus on overcoming multicollinearity challenges. To explore the market dynamics, we conducted four initial experiments in the central regions of Riga and Jelgava (Latvia), as well as São Paulo and Niterói (Brazil). Data were collected from real estate advertisements, supplemented by civil engineering inspections, and analyzed following international valuation standards. The research integrated human decision-making behavior with machine learning and the Apriori algorithm. Our methodology followed five key stages: data collection, data preparation for association rule mining, the generation of association rules, fuzzy logic analysis, and the interpretation of model accuracy. The proposed method achieved a mean absolute percentage error (MAPE) that ranged from 5% to 7%, indicating strong alignment with market trends. These findings offer valuable insights for decision making in urban development, particularly in optimizing renovation priorities and promoting sustainable growth.

Research on English Online Teaching Model Based on Association Rules Driven by Big Data

In today’s information age, big data has become an indispensable and important resource in various fields, and the education sector is no exception. With the explosive growth of educational data, how to effectively mine and utilize this data to optimize the education and teaching process has become a focus of attention for educators and researchers. Among them, association rule mining, as an important data mining technique, is increasingly widely used in the field of education. This investigation delves into the deployment of association rule mining within the framework of English online teaching models, capitalizing on the burgeoning domain of big data. In the era of exponentially advancing information technology, big data has crystallized as an integral component for educational enhancement in both pedagogical quality and methodologies. Initially, this paper dissects a spectrum of extant teaching paradigms propelled by big data analytics. The discourse then pivots to scrutinize the contemporary landscape and the evolution of English online pedagogy. Employing association rule analysis, the study excavates a trove of significant patterns and linkages from voluminous datasets of online educational activities. The insights gleaned serve as a compass for refining instructional strategies and judiciously distributing educational resources. The empirical evidence underscores the proposition that granular examination of student engagement metrics and scholastic achievement empowers educators to tailor bespoke educational trajectories, thereby amplifying pedagogical efficacy and enriching the academic voyage. Beyond furnishing an avant-garde outlook on English online instruction, the findings proffer a substantive benchmark for e-pedagogy across diverse academic disciplines.

Retraction Note: Association rule mining using fuzzy logic and whale optimization algorithm

Retraction Note: Association rule mining using fuzzy logic and whale optimization algorithm

Employing machine learning techniques for prediction of micronutrient supplementation status during pregnancy in East African Countries

Micronutrient deficiencies, known as “hidden hunger” or “hidden malnutrition,” pose a significant health risk to pregnant women, particularly in low-income countries like the East Africa region. This study employed eight advanced machine learning algorithms to predict the status of micronutrient supplementation among pregnant women in 12 East African countries, using recent demographic health survey (DHS) data. The analysis involved 138,426 study samples, and algorithm performance was evaluated using accuracy, area under the ROC curve (AUC), specificity, precision, recall, and F1-score. Among the algorithms tested, the random forest classifier emerged as the top performer in predicting micronutrient supplementation status, exhibiting excellent evaluation scores (AUC = 0.892 and accuracy = 94.0%). By analyzing mean SHAP values and performing association rule mining, we gained valuable insights into the importance of different variables and their combined impact, revealing hidden patterns within the data. Key predictors of micronutrient supplementation were the mother’s education level, employment status, number of antenatal care (ANC) visits, access to media, number of children, and religion. By harnessing the power of machine learning algorithms, policymakers and healthcare providers can develop targeted strategies to improve the uptake of micronutrient supplementation. Key intervention components involve enhancing education, strengthening ANC services, and implementing comprehensive media campaigns that emphasize the importance of micronutrient supplementation. It is also crucial to consider cultural and religious sensitivities when designing interventions to ensure their effectiveness and acceptance within the specific population. Furthermore, researchers are encouraged to explore and experiment with various techniques to optimize algorithm performance, leading to the identification of the most effective predictors and enhanced accuracy in predicting micronutrient supplementation status.

Developing a novel H-Apriori algorithm using support-leverage matrix for association rule mining

Developing a novel H-Apriori algorithm using support-leverage matrix for association rule mining

An efficient and resilience linear prefix approach for mining maximal frequent itemset using clustering

The numerous volumes of data generated every day necessitate the deployment of new technologies capable of dealing with massive amounts of data efficiently. This is the case with Association Rules, a tool for unsupervised data mining that extracts information in the form of IF-THEN patterns. Although various approaches for extracting frequent itemset (prior step before mining association rules) in extremely large databases have been presented, the high computational cost and shortage of memory remain key issues to be addressed while processing enormous data. The objective of this research is to discover frequent itemset by using clustering for preprocessing and adopting the linear prefix tree algorithm for mining the maximal frequent itemset. The performance of the proposed CL-LP-MAX-tree was evaluated by comparing it with the existing FP-max algorithm. Experimentation was performed with the three different standard datasets to record evidence to prove that the proposed CL-LP-MAX-tree algorithms outperform the existing FP-max algorithm in terms of runtime and memory consumption.

Data Mining Technology for Smart Campus in Behavior Association Analysis of College Students

The data in smart campuses is complex and massive, with insufficient utilization, and existing data processing methods have many limitations. Therefore, in order to improve the efficiency of data processing in universities and assist in student management, a data processing method integrating cluster analysis and association rule mining is proposed. The proposed method is divided into two parts. Firstly, an improved K-Means model based on information entropy and density optimization is constructed for clustering analysis of student consumption, learning, and other data; Secondly, use the improved Mapping Apriori to obtain the correlation between student grades, consumption records, and learning behavior. The clustering results on student consumption data show that the average accuracy of ED-K-Means clustering is 97.41%, which is 12.8%, 8.5% and 4.0% higher than the comparison algorithm. The result of the correlation between consumption level and achievement shows that when the amount of consumption is less than 1500 yuan, the student’s achievement is directly proportional to the amount of consumption. Therefore, the proposed method can effectively mine and analyze student behavior data, which has important practical significance for intelligent management in universities.

A micro-network within the port for vessel anchorage selection decision support

Upon arrival at ports, selecting the appropriate anchorage is a critical concern shared by ship captains and port managers. This study aims to exploit the rich geographic and semantic information embedded in historical ship trajectory data to construct a micro-network of port elements and employ network analysis techniques to mine spatial service relationships among port elements, thereby establishing an Anchorage Selection Decision Support (ASDS) model. This process involves extracting vessels’ dwell location information within ports from trajectory data and using this information and its sequence to establish an Anchorages-Berths Micro-Network (ABMN) in port. By applying association rule mining techniques, this research reveals the spatial service relationships within the micro-network between anchorages and berths, and integrates relevant indicators to develop the ASDS model. The aim is to help pilots of approaching vessels in making more rational anchorage choices, thus optimizing the operational efficiency of vessels in ports. The effectiveness of this approach has been validated through experiments conducted in the research region at the Nanjing port of the Yangtze River. This research is significant for exploring the correlation between port anchorages and berths, as well as for selecting appropriate anchorages for vessels arriving at the port.

Applying information mining technology in online entrepreneurship training course

The purpose of this study is to deeply explore the application of information mining technology in online entrepreneurship training courses, and to improve students’ learning effects and entrepreneurship success rate through systematic analysis and optimization of key data in the teaching process. With the development of online education, how to effectively use big data technology to meet personalized learning needs has become an important issue. This study takes several online entrepreneurship training courses as the research object, and uses information mining technology to extract and analyze students’ behavioral data during course participation, including data on study time, interaction frequency, assessment results, etc. Through machine learning algorithms and association rule mining, the research revealed the main factors that affect students’ learning effects and entrepreneurial success, and designed targeted teaching strategies, such as dynamically adjusting learning content, providing personalized feedback, optimizing learning paths, etc. Experimental results show that online courses using information mining technology significantly improve students’ knowledge mastery and entrepreneurial success rate, especially in terms of personalized learning experience and teaching efficiency. In addition, this study also explores the application prospects of information mining technology in future online education. It is believed that through the combination with artificial intelligence (AI) technology, the intelligence and adaptability of online courses can be further enhanced to meet more diversified learning needs.

Supervised, Unsupervised Learning Methods in Data Mining: A Comprehensive Survey

Abstract: Data mining is the area of computational science consider as the subset of Artificial Intelligence, Machine Learning that concentrate on cognizing patterns and structures in data to promote learning, analyzing, and decision making beyond human interaction. In simplest term, data mining allows the user to feed data mining algorithms on an immense amount of data and then make the computer to make data-driven recommendations and decisions based on the input or training data. Data is the essential component of all business. Data mining with supervised and unsupervised methods is the key to unlock the value of business data and keep ahead of the competition. This paper presents a comprehensive survey on Data mining process, supervised, unsupervised methods, through a survey of literature considering the significant research areas using classification, clustering, and association rule mining.

Enhanced Association Rule Mining and Discovering Frequent Patterns in Genomes: Integrated Clustering and Weight Judgement

Enhanced Association Rule Mining and Discovering Frequent Patterns in Genomes: Integrated Clustering and Weight Judgement