Association Rule Mining Research Articles

A Multi-Objective Nutcracker Optimization Algorithm Based on Cubic Chaotic Map for Numerical Association Rule Mining

Traditional numerical association rule mining optimization algorithms have limitations in handling discrete attributes, and they are susceptible to becoming trapped in local optima, uneven population distribution, and poor convergence. To address these challenges, we propose a multi-objective nutcracker optimization algorithm based on a cubic chaotic map (C-MONOA), specifically designed for mining association rules from mixed data (continuous and discrete). Unlike existing models, C-MONOA leverages a chaotic map for population initialization, alongside Michigan rule encoding, to dynamically optimize feature intervals during the optimization process. This algorithm integrates continuous and discrete data more effectively and efficiently. This article uses support, confidence, Kulc metric, and comprehensibility as evaluation indicators for multi-objective optimization. The experimental results show that C-MONOA performs well in rule scoring and can generate frequent, simple, and accurate rule sets. This study extends the association rule mining method for mixed data, demonstrating high performance and robustness and providing new technical tools for application fields such as market analysis and disease prediction.

Insights into the Interactions of Pipeline Risk Factors and Consequences Using Association Rule Mining

Insights into the Interactions of Pipeline Risk Factors and Consequences Using Association Rule Mining

Integrating Protection Motivation Theory With Cultural Context

This study integrates Protection Motivation Theory (PMT) with cultural dimensions to design a cybersecurity education framework tailored to Kuwait. The study uses advanced analytical techniques like K-means clustering and Association Rule Mining (ARM) to find important patterns in how age, education level, and cultural norms (like religious adherence and family reputation) affect how people act online safely. The findings highlight key gaps in awareness and coping mechanisms, informing a framework that incorporates gamification, microlearning, and culturally relevant scenarios. We design these strategies to enhance engagement and foster meaningful behavioral change. Although we developed the framework for Kuwait, its scalable design ensures applicability to other culturally diverse regions, offering a replicable model for improving global cybersecurity awareness and resilience.

Investigation into the evolution of airport cement pavement distresses based on association rule mining

Investigation into the evolution of airport cement pavement distresses based on association rule mining

Exploring performance profiles of home health care agencies: A two-stage analytical approach.

Public reporting of home health care agencies' performance metrics, including patient satisfaction, care processes, and health outcomes, aims to inform customer decisions and encourage agencies to improve the quality of services. However, there is limited research that examines the heterogeneous performance of home health care agencies. The aim of this study was to analyze the performance of home health care agencies by identifying distinct subgroups of agencies with similar performance profiles and describing the relationships between agency characteristics and such subgroups. We propose a two-stage analytical approach employing unsupervised machine learning methods. First, clustering analysis is applied to performance measures, allowing the partitioning of agencies into homogeneous subgroups based on similarities in performance. Then, association rule mining is used to uncover the relationships between cluster assignments and agency characteristics. The two-stage analytical approach identified four clusters with significantly different performance profiles and agency characteristics: cost-efficient agencies with high patient satisfaction (Cluster 1), high-cost agencies with high-quality care (Cluster 2), urban agencies with low patient satisfaction (Cluster 3), and small agencies with low-quality care (Cluster 4). This study contributes to understanding agency performance in the U.S. home health care industry. By identifying distinct subgroups of agencies and understanding the factors influencing their performance, we can enhance home health care services' overall quality and effectiveness. Our study uncovered diverse performance profiles and associated characteristics among home health care agencies, highlighting the need for tailored strategies and targeted interventions to improve the quality of care across clusters. Health care administrators and policymakers should consider cluster-specific recommendations.

Time series data analysis and association rule mining in financial recommendation systems using Hadoop and Spark

Increasing amounts of financial data demand sophisticated analytics to develop sound recommendation models. This article discusses combining time series analysis and association rule mining for big data in Hadoop and Spark to enrich financial product recommendation engines. The paper is an integrated analysis of two types of prediction algorithms: AutoRegressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks to forecast user behavior and demand for financial services in the future from transactional history. The ARIMA model is used as the default while the LSTM model is used to represent non-linear dependencies and give a more dynamic forecast. association rule mining in particular the Apriori algorithm is used to find latent patterns and relationships between user transactions and financial products. This article illustrates how time series forecasting and association rule mining can be merged to bring a more useful financial recommendation. The hybrid approach, which combines both approaches, proves to increase user interaction and recommendation accuracy by 20% compared to the previous systems, according to experiments. The paper emphasises the possibilities of using big data in the construction of scalable, individualized financial recommendation systems.

Innovative Research on Physical Data Management of Football Players Under the Background of Smart Medical Treatment

With the intensification of the competition for public medical resources and the rapid development of smart medical technology, the demand for in-depth analysis of athletes’ basic information and decision support is growing day by day. However, the practice of applying smart medicine to athletes’ physical information management is still relatively limited. This paper aims to explore how to use smart medical decision technology to build an integrated athlete physical information management and decision support platform. Taking football players as the research object, this paper integrates data mining technology into a smart medical decision-making model, to achieve accurate analysis and effective management of athletes’ physical fitness information. The core of the research is to develop a smart healthcare decision model that can identify key information and patterns on the platform through data mining technology. In the selection of the decision algorithm, this paper adopts cluster analysis and association rule mining algorithms, which can deeply dig into the potential rules and correlations in the data. Through the application of an algorithm, we can accurately evaluate the physical condition of football players, and provide scientific guidance and suggestions for their subsequent physical training. The algorithm in this study is not only innovative in theory, but also shows good adaptability and effectiveness in practical application.

Creating informative experiences through a visual and interactive representation of health and social care data

Association rule mining is an established machine learning tool for finding patterns (‘rules’) in big datasets. The algorithm can easily produce a large number of ‘rules’ of how items in a dataset are related to one another. This can pose a significant challenge to the interpretability and usefulness of the results obtained. In this paper, we present how to support decision making through ‘playable mechanics’ powered by a video games engine. The premise is to create aesthetic and informative experiences through a visual and interactive representation of a problem space such as the association rules mined from a health and social care dataset. The Unity game engine is used to create a force-directed graph to be rendered and explored in real-time using the Barnes-Hut method to accelerate computations. A Boolean AND/OR/NOT selection function was implemented, enabling the graph to be explored and pruned to the data points specified by the search query. As a result, users can obtain an overview of the large-scale structure of the dataset, with the option of performing targeted explorations around points of interest. To evaluate the effectiveness of the application, a series of online user testing workshops were conducted. The resultant thematic analysis found the incorporated features to be well-integrated, but a difference was found between the responses of users with high or low technical proficiency within the commissioning organization. The technical users were able to quickly grasp the operation of the system but were unclear about its purpose or practical application. Conversely, the health and social care professionals saw the potential value of the tool but were unsure of their personal ability to use it effectively. Finally, System Usability Scale (SUS) scores were obtained from participants in a final in-person workshop, with excellent results overall (mean 84, top quartile).

Prediction of stunting and its socioeconomic determinants among adolescent girls in Ethiopia using machine learning algorithms.

Stunting is a vital indicator of chronic undernutrition that reveals a failure to reach linear growth. Investigating growth and nutrition status during adolescence, in addition to infancy and childhood is very crucial. However, the available studies in Ethiopia have been usually focused in early childhood and they used the traditional stastical methods. Therefore, this study aimed to employ multiple machine learning algorithms to identify the most effective model for the prediction of stunting among adolescent girls in Ethiopia. A total of 3156 weighted samples of adolescent girls aged 15-19 years were used from the 2016 Ethiopian Demographic and Health Survey dataset. The data was pre-processed, and 80% and 20% of the observations were used for training, and testing the model, respectively. Eight machine learning algorithms were included for consideration of model building and comparison. The performance of the predictive model was evaluated using evaluation metrics value through Python software. The synthetic minority oversampling technique was used for data balancing and Boruta algorithm was used to identify best features. Association rule mining using an Apriori algorithm was employed to generate the best rule for the association between the independent feature and the targeted feature using R software. The random forest classifier (sensitivity = 81%, accuracy = 77%, precision = 75%, f1-score = 78%, AUC = 85%) outperformed in predicting stunting compared to other ML algorithms considered in this study. Region, poor wealth index, no formal education, unimproved toilet facility, rural residence, not used contraceptive method, religion, age, no media exposure, occupation, and having one or more children were the top attributes to predict stunting. Association rule mining was identified the top seven best rules that most frequently associated with stunting among adolescent girls in Ethiopia. The random forest classifier outperformed in predicting and identifying the relevant predictors of stunting. Results have shown that machine learning algorithms can accurately predict stunting, making them potentially valuable as decision-support tools for the relevant stakeholders and giving emphasis for the identified predictors could be an important intervention to halt stunting among adolescent girls.

Longitudinal analysis for predicting amino acid changes in HIV-1 using association rule mining

Longitudinal analysis for predicting amino acid changes in HIV-1 using association rule mining

Risk analysis of Arctic navigation using text mining (TM) and improved association rule mining (ARM) methods

Risk analysis of Arctic navigation using text mining (TM) and improved association rule mining (ARM) methods

Forecasting Trends in Cryptocurrencies through the Application of Association Rule Mining Techniques

Forecasting Trends in Cryptocurrencies through the Application of Association Rule Mining Techniques

GNN 기반 연관 규칙 마이닝을 통한 낙상 위험 포즈 패턴 분석

This study proposes a GNN-based association rule mining method for predicting fall risks. To overcome the limitations of wearable devices, pose data was collected using computer vision techniques and integrated into a GNN model for precise predictions. The use of DeepPose for pose estimation, coupled with FP-growth for association rule extraction, followed by the application of a GAT model, enables the learning of complex interactions between poses. The proposed method demonstrates superior performance, achieving over 98% in Accuracy, Precision, Recall, and F1-Score compared to OpenPose-based approaches. This model highlights the potential for enhancing fall prevention and real-time monitoring in high-risk groups.

Unveiling Patterns: Advanced Data Mining Techniques for Accurate Predictive Analytics

With the rapid increase in the volume, variety and availability of data across organizations, managers are confronted with the task of analyzing data for meaningful information. As today’s undeniable evidence shows, data mining imperatives and its close cousin predictive analytics have become fundamental recognized approaches of operationalizing raw data into strategic decision supports. Data warehousing and data mining are two distinct ideas. Predictive analysis is the process of applying the extracted data model to anticipate possible future events, while data mining is the process of uncovering the hidden facts and model of a massive data pile. Learn more about a variety of specialized data mining techniques that improve the effectiveness of predictive analytics by refining aggregation, decision trees, neural networks, support vector machines, and association rule mining by reading this article.

The Use of Data Mining Procedures in Understanding Customer Inclinations Within the Social and Entertainment Industry

Different techniques and methods have been established that have analysed consumers in shopping patterns and their usability and the real-world solutions required by them. Such information-mining techniques and analyses provide useful insights to organizations that can be really profitable while mining databases; though different techniques have some merit but also some disadvantages when their limitations are considered. Television dominates the entertainment landscape, with service providers always offering diverse programs to suit the tastes of different customers. The choices of viewers vary according to locality and season, and service providers usually bundle channels into pre-defined packages. Data mining techniques can be used to analyse customer preferences, thereby creating new, customized packages or groups of channels that suit individual needs. This paper explores consumer behaviour, focusing on the psychological factors that influence purchasing decisions and demonstrates how data mining methods can improve traditional approaches. Using an experiment based on association rule mining, the study derives rules for identifying trusted customers from sales data in the supermarket industry. Key Words: Consumer behaviour. Data mining, Association rules, Television, Supermarket

Implementasi Data Mining dalam Menganalisis Pola Pembelian Produk Toko Oleh-Oleh Umrah dan Haji

One of the methods that is often applied in Data Mining technology is the Association Rule Mining technique. The Association Rule Mining method aims to find patterns of relationships between items sold in a database and in the retail business is often referred to as Market Basket Analysis. Market Basket Analysis is an analysis method used to understand consumer behavior by identifying purchase patterns from transaction data, besides that this method can also be useful for maintaining the inventory of goods sold. The research methods used are research design methods, method research questions, data collection tools, data analysis, and data validation. The results obtained from this study are the threshold value used is support ≥ 10% and confidence ≥ 30%. The results of the analysis showed the best rule, namely: "Nuts, Honey → Prayer Rugs" with a confidence value of 59%. This means that consumers who buy Nuts and Honey have a 59% chance that they will also buy a prayer mat. This research contributes to efforts to improve the efficiency of stock management and understand consumer behavior through the analysis of purchasing patterns which is ultimately expected to increase customer satisfaction and store sales

A novel approach for unveiling co-occurrence patterns of UV filter mixtures in sunscreens: Prioritization for hazard and risk assessment.

In recent years, concerns regarding the toxicity of organic UV filters in sunscreen products have increased. While sunscreen products contain multiple UV filters in combination, current understanding on their co-occurrence patterns and mixture toxicities is still limited. This study utilized a public database, "Hwahae", and analyzed 2183 sunscreen products marketed in South Korea as of 2019, using an association rule mining (ARM) to elucidate their co-occurrence patterns. We identified twenty-two UV filters in the sunscreen products, with titanium dioxide (TiO2) being the most prevalent (68 %), followed by ethylhexyl methoxycinnamate (EHMC, 60 %) and ethylhexyl salicylate (EHS, 49 %). Sunscreen products typically contained a median of five UV filters per product. Usage patterns of UV filter mixtures varied by the target user group of the product, i.e., general, susceptible, and male users. EHMC and EHS were commonly combined in most products, except those marketed for susceptible users. For susceptible users, inorganic UV filters such as TiO2 and zinc oxide (ZnO) were dominantly used. Combinations of UV filters that provide protection against different types of UV light, e.g., butyl methoxydibenzoylmethane (BMDBM) for UVA, and octocrylene (OCT), EHS, or homosalate (HS) for UVB, were also frequently used together, with high lift values. In conclusion, our study demonstrated that ARM approach can be useful to identify major combinations of UV filters present in sunscreen products, and prioritize the UV filter combinations requiring safety assessment and regulatory attention.

Characterizing Water Users through Frequent Patterns and Association Rules by Using Apriori Algorithm: A Case of Pangani Basin Tanzania

Objectives: To identify the hidden patterns in the K-means clustered dataset for the Pangani Basin using the Apriori algorithm through frequent patterns and association rules to enrich cluster characteristics. Methods: Frequent patterns and association rule mining were used to discover the hidden attributes in the K-means clustered dataset. Measures of minimum support ranging from 0.5% to 5% and minimum confidence ranging from 50% to 100% were used to generate a manageable number of rules which were then filtered for redundancy. Lift value >1.0 was used to determine the rule's interestingness while Arules and ArulesViz in R were used to visualize generated rules. Findings: Clusters one to four generated 25, 31, 47, and 49 rules respectively at a minimum confidence of 50% and minimum support of 2% in the first two clusters and 1% in other clusters. Furthermore, water users in cluster one were observed to abstract more water than the three clusters, while their water use fee also reflected on the amount they abstracted. In clusters two and three, water users identified the same amount of water source capacity but differed in the amount requested and water use fee. Water users in cluster four were identified with less water source capacity and fewer amounts abstracted than other clusters. However, their water use fee identified was higher than those in cluster three, with high water source capacity and high amount requested. Such a difference is attributed to the type of water use for cluster three users being domestically supplied through community water supply entities to help villagers access water. In contrast, the water use for users in cluster four is domestic and commercial. Novelty: When aggregated with the clustering observations, the identified association rules mining results provide a broad understanding of water users' characteristics for better water allocation and rationing. Keywords: Association rule, Frequent Patterns, Apriori, Characterization, Pangani Basin

A Cross-Product Analysis of Earphone Reviews Using Contextual Topic Modeling and Association Rule Mining

Within the evolving field of sentiment analysis, the integration of topic modeling and association rule mining presents a promising yet underexplored method. This approach currently lacks an organized framework for maximizing insights that aid in drawing robust conclusions concerning customer sentiments. Therefore, this study addresses the need and rationale for having comprehensive sentiment analysis systems by integrating topic modeling and association rule mining to analyze online customer reviews of earphones sold on Amazon. It employs Bidirectional Encoder Representations from Transformers for Topic Modeling (BERTopic), a technique that generates coherent topics by effectively capturing contextual information, and Frequent Pattern Growth (FPGrowth), an efficient association rule mining algorithm used for discovering patterns and relationships in a dataset without candidate generation. This analysis of reviews on ten earphone products identified key customer concerns as follows: sound quality, noise cancellation, durability, and battery life. The results indicate an overall positive sentiment towards sound quality and battery life, mixed reviews on noise cancellation, and significant dissatisfaction with product durability. Using integrated topic modeling and association rule mining offers deeper insights into customer preferences and highlights specific areas for product improvement and guiding targeted marketing strategies. Moreover, we focused on algorithm selection to improve the model’s performance and efficiency, ensuring effective compatibility with our sentiment analysis framework. This study demonstrates how combining advanced data mining techniques and structuring insights from written customer feedback enhances the depth and clarity of sentiment analysis, furthering its applicability in e-commerce research.

An Association Rule Mining-Based Modeling Framework for Characterizing Urban Road Traffic Accidents

The World Health Organization has recognized road traffic accidents as a global crisis, particularly in urban environments. Despite extensive research endeavors, significant gaps remain in our understanding of how various factors interact to influence urban road traffic incidents. This study analyzed data from 4285 urban road traffic accidents in Hubei Province, employing a two-step clustering algorithm to classify accidents into distinct groups based on specific conditions. Subsequently, association rule mining was utilized to discern relationships between accident characteristics within each cluster. Additionally, a classification based on the association rule algorithm was implemented to develop a predictive model for analyzing factors contributing to casualties. The data were categorized into clusters based on weather and road conditions, with separate discussions conducted for each scenario. The findings indicated that urban congestion is the most critical factor contributing to accidents. Interestingly, rather than in severe weather, accidents were more prevalent during cloudy, light-rain conditions. Electric vehicles and motorcycles emerged as the most vulnerable groups. Furthermore, a notable interaction was observed between the day of the week, time of day, and weather conditions. The predictive model achieved an impressive average accuracy of 86.9%. This methodology facilitates the identification of contributing factors and mechanisms underlying urban road traffic accidents in China and holds potential for establishing accident analysis models in similar contexts. The interactive visualization of association rules further enhances the applicability of the findings. The findings of this study can provide valuable insights for traffic management authorities to understand the causes of urban road traffic accidents, assisting them in devising effective policy measures and countermeasures to reduce the occurrence of accidents and casualties.