Association Rules Research Articles

Fault diagnosis method of Bayesian network based on association rules

Fault diagnosis method of Bayesian network based on association rules

Data Management Analysis for Predicting Stroke using RapidMiner

Stroke are known as the second most leading cause of death. Because of this, data mining techniques are already being used to predict patients that may have stroke. Therefore, we are doing a study to try using data mining techniques using RapidMiner to find information or patterns regarding stroke from a dataset obtained from Kaggle. Three data mining techniques are used in this study, that is classification using decision trees, association rule using FP-Growth algorithm, and clustering technique using k-Means algorithm. Using RapidMiner, we are able to process the dataset using the operators provided in the application. As the result, we found out that due to an unbalanced data, the decision tree model made were only able to predict 68,75% patients as having stroke. With the association rule technique, we found out that most attributes in the dataset does not really associated with each other. With the clustering technique, we were able to group up patients and found out that most patients that have stroke are averaged in the age of 58, with 31 bmi, and 201 average glucose level.

Analisis Perbandingan Algoritma Apriori dan FP-Growth untuk Menentukan Strategi Penjualan Pada Maestro Jakarta Cafe & Space

There has been a decline in sales at Maestro Jakarta Cafe & Space due to a lot of competition and not optimal management of transaction data so that innovation is needed to increase sales. This study aims to compare the performance and results of the Apriori and FP-Growth algorithms in analyzing sales transaction data to determine the optimal sales strategy. This research uses the Apriori and FP-Growth methods to analyze sales transaction data by applying the Cross-Industry Standard Process for Data Mining (CRISP-DM). The data used is product sales transaction data from November 2023 to April 2024. The results of performance comparisons in processing time speed and memory usage that have been carried out show that in processing time speed the FP-Growth algorithm is slightly faster than the Apriori algorithm while in the use of memory capacity the Apriori algorithm requires a larger memory capacity than the memory capacity used by the FP-Growth algorithm. This shows that the performance of the FP-Growth algorithm is better than the Apriori algorithm. The analysis results of the Apriori and FP-Growth algorithms on sales transaction data using a minimum support value of 1% and a minimum confidence value of 100% resulted in 22 association rules. Both algorithms produce identical rules, with the only difference being the occurrence index. The results of this analysis can be used by Maestro Jakarta Cafe & Space in determining sales strategies.

RETRACTED: Wang et al. Power Cable Status Evaluation Method Based on Electrical Tree Growth and Data Association Rules. Processes 2023, 11, 2900

The journal retracts the article titled “Power Cable Status Evaluation Method Based on Electrical Tree Growth and Data Association Rules” [...]

Pemodelan Data Mining Transaksi Penjualan Menggunakan Algoritma Apriori (Studi Kasus: Kedai Ngodeng & Smoothies)

The role of UMKM is very large in the growth of the Indonesian economy, Kedai Ngodeng & Smoothies, as one of the UMKM that is active in the food and beverage industry. This shop has not made maximum use of sales transaction data. The transaction data is only used as an archive and is not used to organize sales strategies. In this research, the method used is the a priori algorithm to determine the relationship between one product item and other product items so that it can be used as a promotional package or bundling to increase sales profits. The data used is sales transaction data for the Ngodeng & Smoothies shop from January to May 2024. The research began with initial data collection, followed by data description and evaluation of data selection. After that, attribute selection is carried out. The CRISP-DM method is applied in this research which includes six stages, namely business understanding, data understanding, data preparation, modeling, evaluation and dissemination. Based on the results of the research, there are 95 Association Rules based on previously determined parameters, namely the minimum support value is 50% and the minimum confidence is 90%. One of the rules that was formed is that Smoothies Mangga Large, Twister, Kembang Cumi --> Odeng Spicy has a confidence value of 100%. The information obtained can provide important insights into customer preferences and can be used to design more effective product bundling promotions.

EVALUATING THE IMPACT OF LEARNING ACTIVITIES ON QUIZ SUBMISSION IN LEARNING MANAGEMENT SYSTEM (LMS)

With the rise of educational technology, the trend of online learning is gradually becoming popular. Quiz submissions are a crucial assessment activity on Learning Man- agement Systems (LMS). This study investigates the factors influencing student quiz sub- missions in online learning environments. An Apriori Algorithm-Based Association Rule Analysis to assess the impact of various learning activities on student quiz submissions has been used in this article with actual online learning datasets. Our data mining results reveal that announcements and conferences have a significant positive influence on quiz submis- sions. In contrast, activities like page lectures, discussions, and reflective assignments may not substantially impact student submission behavior. The results of this research can be used to recommend teachers to organize appropriate learning activities on LMS to promote students’ quiz submission. Keywords: Data Mining in Education; Online Learning Activities; Online Assign- ment Submission

Mining of dynamic traffic-meteorology-atmospheric pollutant association rules based on Eclat method

With the rapid increase of urban vehicles, the atmospheric compound pollutants, notably PM2.5 and O3, have significantly increased and seriously affected public health. Traffic and meteorological conditions are the primary influencing factors of pollutant concentrations, and their spatial and temporal changes affect the dispersion of pollutants. Increasing use of high-resolution big data offers opportunities to explore these correlations. More extensive quantitative studies are essential for effective air pollution control. This study presents an Eclat algorithm to quantitatively reveal the relationship between traffic, meteorology and pollutants with hourly and 5-minute scale data in the urban area of Guangzhou. We establish a research framework covering temporal pollution analysis, multifactor rule mining, and spatial effects. The results show that PM2.5 and O3 exhibit coordinated trends on the daily scale influenced by traffic flow and meteorology conditions, but on the hourly scale, they are negatively correlated. At the 5-minute scale, synchronized variations occur only during specific periods. This finer scale better identifies association rules for high-concentration pollutant scenarios, and non-roadside sites outperform roadside sites in mining these associations. For example, when humidity is below 37%, atmospheric pressure is 1016.2–1020.3 Pa, wind speed is 1.7–2.6 m/s, and the traffic volume on Jiefang North Road exceeds 635 vehicles every 5 min, there is a 92.86% probability that the PM2.5 concentration at GYQ (a non-roadside monitoring site) will exceed 127 μg/m3. These findings enhance our understanding of how dynamic traffic and meteorological conditions impact atmospheric pollutants and provide a scientific basis for regional collaborative pollution prevention.

An Investigation of AI-Enabled Comprehensive Survey of Phishing Attacks detection techniques

Phishing is an online criminal activity which traps regular online customers to reveal their sensitive information into fake destinations by disguising themselves as original website. Because of the short life expectancy of phishing sites and the fast progression of phishing systems the current anti- phishing solutions are either unfit to manage the rising changes or in fit for assuming a successful part against this attack.Natural language handling and machine learning – these are the centre usage of the multi-stage approach proposed to identify phishing attacks and to perceive the substance/association that has been misused by the attackers to execute the phishing attacks. In this technique the primary stage is the revelation of named elements (names of areas, individuals and associations) and afterward the disclosure of concealed topics and for this the strategies that backings both phishing and non-phishing information therefore, Conditional Random Field (CRF) and Latent Dirichlet Allocation (LDA) is utilized. Next stage is the AdaBoost arrange where the named elements and the concealed subjects are dealt as features and the messages are ordered into phishing or non-phishing. proposed techniques are planned to alleviate the effect of phishing attack. The principal strategy proposes is to identify Wi-Fi phishing utilizing Association Rule mining. A Wi-Fi hotspot which associates such hand held gadgets has turned into a transitory archive of delicate data, in this way giving an open door for programmers to gather money related gain .Consequently data security arrangements tending to Wi-Fi hotspots (Wi-Fi phishing attack) has turned into the need of great importance. The conceivable approaches to attack the security of Wi-Fi hotspots and counter-attack methodologies have been tended to in this strategy. Key works: Phishing, LDA,AI

Decomposing maintenance actions into sub-tasks using natural language processing: A case study in an Italian automotive company

Industry 4.0 has led to a huge increase in data coming from machine maintenance. At the same time, advances in Natural Language Processing (NLP) and Large Language Models provide new ways to analyse this data. In our research, we use NLP to analyse maintenance work orders, and specifically the descriptions of failures and the corresponding repair actions. Many NLP studies have focused on failure descriptions for categorising them, extracting specific information about failure, or supporting failure analysis methodologies (such as FMEA). Whereas, the analysis of repair actions and its relationship with failure remains underexplored. Addressing this gap, our study makes three significant contributions. Firstly, we focused on the Italian language, which presents additional challenges due to the dominance of NLP systems that are mainly designed for English. Secondly, it proposes a method for automatically subdividing a repair action into a set of sub-tasks. Lastly, it introduces an approach that employs association rule mining to recommend sub-tasks to maintainers when addressing failures. We tested our approach with a case study from an automotive company in Italy. The case study provides insights into the current barriers faced by NLP applications in maintenance, offering a glimpse into the future opportunities for smart maintenance systems.

Big data classification using SpinalNet-Fuzzy-ResNeXt based on spark architecture with data mining approach

In the modern networking topology, big data is highly essential for several domains like e-commerce, healthcare, and finance. Big data classification has offered effectual performance in several applications. Still, big data classification is highly difficult and the recognized classification approaches require a longer duration and numerous resources for executing the accessible data. For resolving such issues, the spark-based classification approach is required. In this work, the hybrid SpinalNet-Fuzzy-ResNeXt model called SFResNeXt is implemented to classify the big data. Here, the SpinalNet and ResNeXt are merged, where the layers are fused with the fuzzy concept. The initial process is the outlier detection. The Holoentrophy method is used to detect the outlier data, and it is removed. Moreover, duplicate detection is performed by fingerprinting approach to detect the repeated data. The, Association Rule Mining (ARM) method is employed for feature selection. The big data is classified by the SFResNeXt. Furthermore, the SFResNeXt-based big data classification offered the accuracy, sensitivity, and specificity of 0.905, 0.914, and 0.922 using the heart disease dataset.

Mining Association Rules on Time-sensitive Data

Real-world data is continuously produced andcollected quickly; hence, mining association rules are used tofind relationships between item sets. One of these combinedrule mining techniques is Rare Association Rule Mining(RARM), a method that extracts rare, combined rules with lowsupport but high confidence from the database. Additionally,a large amount of time-sensitive data is created in many fields,and combining valid and outdated data leads to low efficiencyin extracting combined rules. This paper explores a miningmethod that increases rare, combined rules on time-sensitivedata to extract common and rare patterns in the database.The primary approach of the research proposes a new treestructure called ILC-tree, inspired by the LC-tree structurepublished in 2022. Specifically, our approach includes a newtree structure and a fast reconstruction method. The proposedapproach reduces execution time and memory consumptionby approximately 2 times compared to the old tree structure.

Research On Purchasing Behavior and Marketing Strategy Optimization of Commercial Medical Insurance Based on Unsupervised Learning Algorithms

Being a marketer in today's world is not an easy job, especially in the insurance industry, where many factors affect whether customers buy insurance and how much they buy. This field also has many research topics and areas worth exploring. In this article, the data used is real insurance sales data and used unsupervised learning methods in machine learning to clean, mine and analyze the data. First, using clustering methods to divide people who buy insurance into three categories based on weight and insurance expenses. Then using association rule learning methods to analyze the recurring features among people who buy insurance. Finally, based on these results, many policies and suggestions can be given from the perspective of insurance companies and sellers.

Polynomial fuzzy information granule based [formula omitted]-triple I fuzzy reasoning algorithm and its short-term forecasting of time series

Short-term forecasting of time series is an important research topic, which involves data characteristic capture and intelligent reasoning. For this topic, the Gaussian polynomial fuzzy information granule with interpretability is granulated on data, enabling the extraction of data trend characteristic, besides, the associate characteristic within the data is captured through building fuzzy association rule. Building upon the data characteristics captured in fuzzy information granule and fuzzy association rule, an intelligent reasoning algorithm called the fuzzy information granule based α-Triple I algorithm is proposed, where the membership degree of data to granule is considered in reasoning, and next the accurate level of deviation from data to granule can be inferenced. Based on the excavated data characteristics and a rational inference, a short-term forecasting model is established. Its superiority in terms of accuracy and reliability when compared to 7 other models in real time series has been tested. Notably, the prediction of the novel model is accurate because the function of FAR is identified from FAR’s truth degree, which means the validity degree of prediction. The application of the proposed model for short-term forecasting holds a potential impact across various fields.

Uncovering patterns of disadvantage in student socioeconomic data using k-means clustering and association rule mining

This study employs a combination of k-means clustering and association rule mining to analyze socioeconomic data from 507 students at a community school in Udon Thani Province, Thailand, highlighting the pronounced economic inequalities impacting educational opportunities. By integrating these data mining techniques, the research categorizes students into four income-based clusters and identifies critical socioeconomic attributes influencing educational access and quality. The application of k-means clustering revealed four distinct economic categories within the student population, with the lowest income cluster significantly lacking in basic amenities such as air conditioning and agricultural vehicles, which are essential for their living and educational environments. Association rule mining was then applied to identify household attributes linked to the ‘very low’ income classification. Fourteen association rules were discovered, with support values ranging from 0.43 to 0.44, confidence levels between 0.81 and 0.82, and lift values consistently at 1.1. Key findings reveal that the absence of basic amenities such as air conditioning, agricultural vehicles, computers, and televisions are strongly associated with the lowest socioeconomic status. For instance, the rule {AirCondition=N, TV=N} => {Cluster=Very Low} had a support of 0.44, confidence of 0.81, and lift of 1.1, indicating households lacking these items have an 81% probability of being classified in the ‘very low’ income group. Notably, the presence of electricity did not correlate directly with higher income clusters, indicating that basic infrastructure access alone does not mitigate the broader socioeconomic challenges. These findings underscore the complex interplay between income levels and access to educational and basic living resources. The study’s implications are profound, advocating for targeted educational policies and interventions that address the nuanced needs of economically disadvantaged students, thereby enhancing their educational outcomes and socioeconomic mobility. Future research directions include broadening the dataset to multiple schools across different regions and conducting longitudinal studies to reveal long-term effects of socioeconomic factors on education.

Intelligent Identification of Hidden Dangers in Hydrogen Pipeline Transmission Station Using GWO-Optimized Apriori Algorithm

This work proposes an intelligent grey-wolf-optimizer-improved Apriori algorithm (GWO-Apriori) to mine the association rules of hidden dangers in hydrogen pipeline transmission stations. The optimal minimum support and minimum confidence are determined by GWO instead of the time-consuming trial approach. Experiments show that the average support and average confidence of association rules using GWO-Apriori increase by 29.8% and 21.3%, respectively, when compared with traditional Apriori. Overall, 59 ineffective association rules out of the total 105 rules are filtered by GWO, which dramatically improves data mining effectiveness. Moreover, 23 illogical association rules are excluded, and 12 new strong association rules ignored by the traditional Apriori are successfully mined. Compared with the inefficient and labor-intensive manual investigation, the intelligent GWO-Apriori algorithm dramatically improves pertinency and efficiency of hidden danger identification in hydrogen pipeline transmission stations.

Creating Personalized Higher Education Teaching System Using Fuzzy Association Rule Mining

Universities and colleges aim to provide students with a solid academic foundation. Quality instruction is one strategy for achieving the highest possible standard in the higher education system. Personalized teaching caters to each student by adapting the learning pace and method to their specific requirements. However, the present state of customized education in higher education resources prevents proper resources from being extracted due to a lack of multi-dimensional association analysis between students, circumstances, and materials. A hybrid personalized teaching system utilizing fuzzy association rules mining is the goal of this research to improve learning in higher education. Effective multi-dimensional association analysis among students, settings, and instructional materials is facilitated by the fuzzy association rules mining-based hybrid personalized teaching system (FARM-HPT). The proposed study conforms to AI standards, is based on fuzzy logic theories, and guarantees precise university-level resource discovery. The study builds on earlier work in data mining by presenting a new, learner-specific recommendation model for personalized teaching that uses FARM to ensure accurate resource recognition and efficient mining of instructional assets at the higher education level. This new approach generates fewer set comparisons and does them faster than the current standard. Focusing on experimental validation, the study shows that the FARM-HPT system can generate individualized lessons while overcoming the constraints of traditional information mining methods. These findings align with AI standards, which shows how important it is to validate new AI approaches using robust empirical evidence. The system ensures effective accuracy on various datasets: LFW (89.76%), JAOLAD (94.43%), OECD (95.43%) and OULAD (97.45%).

Global Suicide Mortality Rates (2000-2019): Clustering, Themes, and Causes Analyzed through Machine Learning and Bibliographic Data.

Suicide research is directed at understanding social, economic, and biological causes of suicide thoughts and behaviors. (1) Background: Worldwide, certain countries have high suicide mortality rates (SMRs) compared to others. Age-standardized suicide mortality rates (SMRs) published by the World Health Organization (WHO) plus numerous bibliographic records of the Web of Science (WoS) database provide resources to understand these disparities between countries and regions. (2) Methods: Hierarchical clustering was applied to age-standardized suicide mortality rates per 100,000 population from 2000-2019. Keywords of country-specific suicide-related publications collected from WoS were analyzed by network and association rule mining. Keyword embedding was carried out using a recurrent neural network. (3) Results: Countries with similar SMR trends formed naturally distinct groups of high, medium, and low suicide mortality rates. Major themes in suicide research worldwide are depression, mental disorders, youth suicide, euthanasia, hopelessness, loneliness, unemployment, and drugs. Prominent themes differentiating countries and regions include: alcohol in post-Soviet countries; HIV/AIDS in Sub-Saharan Africa, war veterans and PTSD in the Middle East, students in East Asia, and many others. (4) Conclusion: Countries naturally group into high, medium, and low SMR categories characterized by different keyword-informed themes. The compiled dataset and presented methodology enable enrichment of analytical results by bibliographic data where observed results are difficult to interpret.

Two stages data mining analytics for food intentional and behavioral recommendations

A recommender system is an information filtering system used to predict a user’s rating or preference for an item. Dietary preferences are often influenced by various etiquettes and culture, such as appetite, the selection of ingredients, menu development, cooking methods, choice of tableware, seating arrangement of diners, order of eating, etc. Food delivery service is a courier service in that delivers food to customers by restaurants, stores, or independent delivery companies. With the continuous advances in information systems and data science, recommender systems are gradually developing towards to intentional and behavioral recommendations. Behavioral recommendation is an extension of peer-to-peer recommendation, where merchants find the people who want to buy the product and deliver it. Intentional recommendation is a mindset that seeks to understand the life of consumers; by continuously collecting information about their actions on the internet and displaying events and information that match the life and purchase preferences of consumers. This study considers that data targeting is a method by which food delivery service platforms can understand consumers’ dietary preferences and individual lifestyles so that the food delivery service platform can effectively recommend food to the consumer. Thus, this study implements two stages data mining analytics, including clustering analysis and association rules, to investigate Taiwanese food consumers (n= 2,138) to investigate dietary and food delivery services behaviors and preferences to find knowledge profiles/patterns/rules for food intentional and behavioral recommendations. Finally, discussion and implications are presented.

Analysis on typical characteristics and causes of coal mine gas explosion accidents in China.

Large-scale coal mine gas explosion (CMGE) accidents have occurred occasionally and exerted a devastating effect on society. Therefore, it is essential to systematically identify the characteristics and association rules of causes of CMGE accidents through analysis on large-scale CMGE accident reports. In this study, 298 large-scale CMGE accidents in China from 2000 to 2021 were taken as the data sample, and mathematical statistical methods were adopted to analyze their general characteristics, coupling cross characteristics, and characteristics of gas accumulation and ignition sources. Moreover, the text mining technology and the Apriori algorithm were used for exploring the formation mechanism of CMGE accidents, during which 46 main causal factors were identified and 59 strong association rules were obtained. Furthermore, an accident causation network was constructed based on the co-occurrence matrix. The key causal items and sets of CMGE accidents were clarified through network centrality analysis. According to the research results, electrical equipment failure, cable short circuit, mine lamp misfire, hot-line work, and blasting spark are the key ignition sources of CMGE. Fan failure, airflow short circuit, and local ventilation fan damage are the main causes of gas accumulation. Besides, the confidence levels of two association rules of "static spark-fan failure" and "blasting spark-airflow short circuit" are higher than 70%, indicating that they are the two dominant risk-coupling paths of gas explosions. In addition, six causes appear frequently in the shortest risk paths of gas explosion and are closely related to other causes, i.e., fan failure, local ventilation fan damage, static sparks, electrical equipment failure, self-heating ignition, and friction impact sparks. This study provides a new perspective on identifying causes of accidents and their complex association mechanisms from accident report data for practical guidance in risk assessment and accident prevention.

Color Analysis of Brocade from the 4th to 8th Centuries Driven by Image-Based Matching Network Modeling

To achieve the color matching rules for the textiles discovered during Silk Road excavations between the 4th and 8th centuries, this research proposed an image-based matching network modeling method. The Silk Road facilitated trade and cultural exchange between the East and West, and the textiles found along the way depict the development of fabrics in a color scheme with great cultural significance. A total of 165 images with brocade patterns were collected from a book with a detailed description of the Western influences on textiles along the Silk Road. Two different clustering methods, including the K-means clustering method and octree quantization approach, were used to extract the primary and secondary colors. By combining the HSV color space with the PCCS color system, the color distribution was analyzed to discover the features of representative color patterns. The co-occurrence relationship of the auxiliary colors was explored using the Apriori algorithm, and a total of eight association rules were established. The results showed that the K-means clustering algorithm can show a better effect of color classification to obtain three primary colors and nine secondary colors. The matching mechanism with a visualized network model was also proposed, which showed that reddish-yellow tones are the main colors in the brocade patterns, and the light and soft tones separately account for 27% and 20%. Beige and brown are the most common colorways, with a confidence level of 47%. One style of brocade pattern was used to demonstrate different appearances within various color networks, which could be applied to 3D virtual fitting. This image-based matching network modeling approach makes the color matching schemes visible, and can assist fashion design with fabric features influenced by historical and cultural development.