Association Rule Mining Approach Research Articles

Investigating the contributing factors of crashes on interstate bridges in Louisiana using latent class clustering and association rule mining

Drivers on long bridges on interstates often face unique challenges, such as restricted lane width, inadequate shoulders, and a lack of clear zones for safe recoveries. Studies on understanding the factors that contribute to crash severity on such high-risk sections of interstates are limited. This research study applies latent class clustering (LCC) to detect homogeneous clusters while accounting for unobserved heterogeneity in a dataset of 10,036 crashes that occurred over a 6-year period (2015–2020) on eight selected bridges. Utilizing the LCC method, the research identifies four optimal clusters in bridge crashes, characterized by attributes such as ‘4-lane,’ ‘6-lane,’ ‘single-vehicle crashes,’ and ‘unknown driver.’ The Association Rule Mining (ARM) approach is used to identify the important collective factors to visible injury (KAB – fatal, severe and moderate) and property damage only (PDO or no injury). In Cluster 1 (4-lane), KAB and PDO crashes differ in collision type and visibility conditions, with rear-end crashes linked to KAB and sideswipe crashes to PDO. Cluster 2 (6-lane) shows similar distinctions but lacks specific lighting associations for PDO. In Cluster 3 (single-vehicle crashes), KAB involves moderate traffic and low visibility, while PDO has lower speed limits and non-dry surfaces. Cluster 4 (unknown driver), despite overrepresenting hit-and-run cases, underscores challenges in injury crash data collection in high-volume mobility scenarios. The discussions of the findings on the severity factors in this study are expected to help traffic safety engineers, policymakers, and planners to identify effective safety countermeasures on major elevated sections.

Understanding risky driving behaviors among young novice drivers in Nigeria: A latent class analysis coupled with association rule mining approach

Traffic crashes are significant public health concern in Nigeria, particularly among young drivers. The study aims to explore the underlying pattern of risky driving behaviors and the associations with demographic factors among young drivers in Nigeria. A combined approach of Latent Class Analysis (LCA) and Association Rule Mining is applied to the dataset comprising responses from 684 young drivers who complete the “Behavior of Young Novice Drivers Scale” (BYND) questionnaires. The LCA identifies four distinct classes of drivers based on the risky behavior profiles: Reckless-Speedsters, Cautious Drivers, Distracted Multitaskers, and Emotion-impacted Drivers. Association rule mining further connects these driver classes to demographic and driving history variables, uncovering intriguing insights. Reckless-Speedsters predominantly consist of young males who engage in riskier driving behaviors, including exceeding speed limits and disregarding traffic rules. Conversely, Cautious Drivers, also predominantly young males, exhibit a safer driving profile marked by rule adherence and a notably lower crash rate. Distracted Multitaskers, sharing a demographic profile with Cautious Drivers, diverge significantly due to their higher crash involvement, hinting at a propensity for distracted driving practices. Lastly, Emotion-Impacted Drivers, primarily comprising young employed males, display behaviors influenced by emotions, shorter driving distances, and prior unsupervised driving experience. Most of the behaviors are attributed to inadequate traffic control, absence of traffic signs in most of the roads, preferential treatment, and lack of strict law enforcement in the country. The findings hold substantial implications for road safety interventions in Nigeria, urging targeted approaches to address the unique challenges presented by each driver class. With acknowledging the study limitations and advocating for future research in objective measures and emotion-behavior interactions, the comprehensive approach provides a robust foundation for enhancing road safety in the Nigerian context.

Source Domain Associations as Conceptual Assemblages in Trauma Talk – an Association Rule Mining Approach

ABSTRACT The qualitative nature of co-deployed or “associated” metaphorical source domains in discourse has been extensively researched, often in terms of whether they share common conceptual roots. There are however few empirical studies on the strength of these associations and their implications. In mental healthcare activities like psychological interviews and counseling, for example, strongly associated sources may suggest unique “conceptual assemblages” that highlight underexplored (dis)similarities between clients or client groups, beyond the typical focus on isolated sources and frequencies. Our case study compares source domain associations produced by two interviewee groups – one meeting the diagnostic threshold for acute stress disorder (ASD), the other not – relating their first-hand experiences of traumatic events during the 2019 Hong Kong social unrest. The machine learning method of association rule mining is used to extract and quantify the top association rules in both groups. Results show i) a shared rule suggesting a highly schematic construal of trauma that nevertheless varies in instantiating details, and ii) several distinct rules that corroborates and lends further insight into the “agentive vs. non-agentive” characteristics exhibited by ASD vs. non-ASD individuals. Implications and future directions, including potential extensions to other discourse contexts, are discussed.

PENERAPAN METODE ASOSIASI PADA ANALISA POLA PEMINJAMAN BUKU PERPUSTAKAAN

The library produces a lot of book loan transaction data every day, but the data has not been maximally utilized due to the limited knowledge of the data, therefore the librarian cannot provide the right book recommendations for readers. The research aims to analyze book loan data by applying the Knowledge Discovery in Database (KDD) method. The research stages are observation and interviews, data selection and data preprocessing, data transformation. Data processing using the apriori algorithm association rule mining approach to provide an overview in seeing the pattern of book loan transactions. This is to provide book recommendations that match the reading interests of library members, so that it can become a reference in the layout of books on the shelf according to the results of the rules formed. The book loan transaction data used is the September period of 2023, the implementation uses the rapidminer application to find association rules. The results obtained as many as 77 rule recommendations with the highest support value of 10.7%, the highest confidence value of 100% and the highest lift value of 14. The rule formed is that if a library member borrows a book by Dale Carneige, the chances that the library member will also borrow a book by George Orwel are 100%. The results obtained can be a reference for the library to provide book recommendations to readers, maintain the availability of book stock and arrange the placement of these books on adjacent shelves.

Quality Analysis of Telemetry Tracking and Command at Ground Stations using the Association Rule Mining Approach

LAPAN built several remote ground stations to support the telemetry tracking and command (TTC) system for the LAPAN-A2 and LAPAN-A3 satellites. These remote ground stations are located in Kototabang/KT (West Sumatra), Biak/BK (Papua), Parepare/PR (South Sulawesi), Rumpin/RP, Rancabungur/RB (Bogor, West Java), and Svalbard/SV (Norway). Problems that often arise in the TTC process are telecommands not being sent (commands sent from the ground station to the satellite) or telemetry packages not being received (feedback on telecommands sent by the satellite to the ground station). This research attempted to calculate and analyze the quality of TTC using a data-mining approach, i.e., rule mining. The calculations were performed using five main parameters: satellite name, ground station, azimuth, altitude, and communication status. The research output consisted of a combination of remote ground station parameters that may result in a successful or failed TTC. For the LAPAN-A3 satellite at the Svalbard ground station, 19 failed communication combinations were generated with a dataset of 57,029. Communication failures occur in azimuth and elevation, i.e., areas blocked by obstacles.

A First-Out Alarm Detection Method via Association Rule Mining and Correlation Analysis.

Alarm systems are commonly deployed in complex industries to monitor the operation status of the production process in real time. Actual alarm systems generally have alarm overloading problems. One of the major factors leading to excessive alarms is the presence of many correlated or redundant alarms. Analyzing alarm correlations will not only be beneficial to the detection of and reduction in redundant alarm configurations, but also help to track the propagation of abnormalities among alarm variables. As a special problem in correlated alarm detection, the research on first-out alarm detection is very scarce. A first-out alarm is known as the first alarm that occurs in a series of alarms. Detection of first-out alarms aims at identifying the first alarm occurrence from a large number of alarms, thus ignoring the subsequent correlated alarms to effectively reduce the number of alarms and prevent alarm overloading. Accordingly, this paper proposes a new first-out alarm detection method based on association rule mining and correlation analysis. The contributions lie in the following aspects: (1) An association rule mining approach is presented to extract alarm association rules from historical sequences based on the FP-Growth algorithm and J-Measure; (2) a first-out alarm determination strategy is proposed to determine the first-out alarms and subsequent alarms through correlation analysis in the form of a hypothesis test on conditional probability; and (3) first-out rule screening criteria are proposed to judge whether the rules are redundant or not and then consolidated results of first-out rules are obtained. The effectiveness of the proposed method is tested based on the alarm data generated by a public simulation platform.

A novel software defect prediction approach via weighted classification based on association rule mining

Software defect prediction technology is used to assist software practitioners in effectively allocating test resources and identifying hidden defects in a timely manner. However, the prediction of defect-prone software using association rule mining algorithms is limited because of the unbalanced distribution of defect data. Furthermore, although the existing weighted association rule mining approach considers item strength, the weight calculation still relies on expert experience and lacks fine granularity. We propose a novel software defect prediction approach based on mutual information and correlation coefficient weighted class association rule mining (MCWCAR). The MCWCAR model employs a cost-sensitive strategy and generates frequent itemsets according to three mining objectives while maintaining the original item distribution: defective class rules, non-defective class rules, and feature association relationships. During the weighted frequent itemset mining process, it combines feature selection and itemset screening to determine the appropriate feature combination through mutual information weighted support. Meanwhile, the correlation coefficient is applied to accurately depict the correlation between feature items and defect classes, serving as the weight to mine class association rules. Additionally, to ensure that interestingness measures have asymmetry and effectively represent negative associations under the condition of class imbalance, the addedvalue is adopted in the filtering association rules. We conducted experiments on 27 open-source datasets and evaluated the performance differences between MCWCAR and state-of-the-art baseline classifiers. Experimental results demonstrate that the proposed algorithm significantly outperforms other baselines in terms of Balance, Gmean, MCC, and F-measure.

The Impact of Socio-demographic Factors on the Prevalence of Hepatitis B in Sokoto, Nigeria: An Association Rule Mining Approach

The Impact of Socio-demographic Factors on the Prevalence of Hepatitis B in Sokoto, Nigeria: An Association Rule Mining Approach

Risk and Protective Factors of Depression in Family and School Domains for Chinese Early Adolescents: An Association Rule Mining Approach.

Depression is one of the most common psychological problems in adolescence. Familial and school-related factors are closely related to adolescents' depression, but their combined effects need further examination. The purpose of this study was to explore the combined effects of risk/protective factors of depression in family and school domains using a sample of Chinese adolescents differing in gender, age group and left-behind status. A total of 2455 Chinese students in primary and secondary school participated in the cross-sectional survey and reported multiple risk/protective factors in family and school environments and depressive symptoms. Association rule mining, a machine learning method, was used in the data analyses to identify the correlation between risk/protective factor combinations and depression. We found that (1) Family cohesion, family conflict, peer support, and teacher support emerged as the strongest factors associated with adolescent depression; (2) The combination of these aforementioned factors further strengthened their association with depression; (3) Female gender, middle school students, and family socioeconomic disadvantages attenuated the protective effects of positive relational factors while exacerbating the deleterious effects of negative relational factors; (4) For individuals at risk, lack of mental health education resources at school intensified the negative impact; (5) The risk and protective factors of depression varied according to gender, age stage and left-behind status. In conclusion, the findings shed light on the identification of high-risk adolescents for depression and underscore the importance of tailored programs targeting specific subgroups based on gender, age, or left-behind status.

Measuring the Influence of Multiscale Geographic Space on the Heterogeneity of Crime Distribution

Urban crimes are not homogeneously distributed but exhibit spatial heterogeneity across a range of spatial scales. Meanwhile, while geographic space shapes human activities, it is also closely related to multiscale characteristics. Previous studies have explored the influence of underlying geographic space on crime occurrence from the mechanistic perspective, treating geographic space as a collection of points or lines, neglecting the multiscale nature of the spatial heterogeneity of crime and underlying geographic space. Therefore, inspired by the recent concept of “living structure” in geographic information science, this study applied a multiscale analysis method to explore the association between underlying geographic space and crime distribution. Firstly, the multiscale heterogeneity is described while simultaneously considering both the statistical and geometrical characteristics. Then, the spatial association rule mining approach is adopted to quantitatively measure the association between crime occurrence and geographic space at multiple scales. Finally, the effectiveness of the proposed methods is evaluated by crime incidents in the city of Philadelphia. Experimental results show that crime heterogeneity is indeed closely related with the spatial scales. It is also proven that the influence of underlying geographic space on crime heterogeneity varies with the spatial scales. This study may enrich the methodology in crime pattern and crime explanation analysis, and it provides useful insights for effective crime prevention.

Characteristics analysis of intercontinental sea accidents using weighted association rule mining: Evidence from the Mediterranean Sea and Black Sea

With the continuous growth of international trade and the number of ships, the pressure on marine safety is increasing. To strengthen the efficiency and pertinence of accident prevention and control measures, in-depth research on the characteristics of marine traffic accidents is of great significance. This study uses a Weighted Association Rule Mining (WARM) approach to investigate the association between marine traffic accidents characteristics. The Mediterranean Sea and Black Sea play a crucial role in global maritime transportation. The marine traffic accidents in the Mediterranean Sea and Black Sea region from 2006 to 2020 are collected from the Lloyd's List Intelligence (LLI) database. Then, WARM is applied to obtain the characteristics of marine traffic accidents. The findings show that Flag of Convenience (FOC) vessels in the Mediterranean have a lower accident rate than non-FOC vessels do. Moreover, the results reveal a strong relationship between accidents and the age, gross tonnage, and ship type. Older general cargo ships with a gross tonnage between 500 and 3000 tons are more prone to accidents. This research provides insights for authorities to develop targeted measures for preventing specific types of accidents and enhancing marine safety.

Modified Genetic Algorithm and Association Rule Mining for the Retail Sector

This paper concentrates on the optimization of elementary association rule mining. The basic approach of association rule mining generates the positive association rule but focusing on both positive and negative association rule mining to find out efficient results is lacking. Thus our aim is to provide an approach to optimize all positive and negative association rules with the help of a modified genetic algorithm. A genetic algorithm is an optimization technique that provides the best possible solutions that are stronger than the other solutions. The present approach focuses on the importance of population through mean fitness value for further genetic algorithm operation. This paper also shows a comparison between normal Apriori, the Genetic Algorithm, and our proposed algorithm. Where in as a result the proposed approach worked better than others. We believe that the proposed methodology would increase the efficiency of the Decision support system of retail stores.

Discovering Hidden Associations among Environmental Disclosure Themes Using Data Mining Approaches

Environmental concerns play a crucial role in sustainability and public opinion on supply chains. This is why, how, and to what extent the firms experience environmental-related actions and inform their stakeholders, which is under discussion by most researchers. This paper aims to leverage data mining and its capabilities by applying association rule mining to the environmental disclosure context. With the aim of extracting hidden relationships between environmental disclosure themes for BIST 100 firms serving the Turkish supply chain, this research implements a novel association rule mining approach and uses the Apriori algorithm. With this purpose, the environmental information of BIST 100 firms was collected manually from sustainability reports; the raw data were processed; and the following seven themes identified the representing firms’ disclosure items: environmental management, climate change, energy management, emissions management, water management, waste management, and biodiversity management. The results indicate various hidden relations between the sector and disclosures, allowing us to generate sector-based rules between environmental disclosure themes.

An association rule mining approach to predict alarm events in industrial alarm floods

Industrial operators often experience overwhelming situations during ongoing alarm floods due to high alarm rates. In such situations, real-time assistance in the form of prediction of upcoming alarm events can ease off the decision-making for industrial operators. Accordingly, this work studies alarm prediction in alarm flood situations, and the main contribution lies in a novel association rule mining approach for real-time prediction of alarm events and their corresponding times of annunciation during an ongoing alarm flood. The proposed method is capable of performing predictions at the triggering instant and modifying the predictions with the increasing of the ongoing alarm flood. The proposed method is implemented mainly in the following steps: (1) A Compact Prediction Tree (CPT) model is modified with new features, namely, the time table and co-occurrence matrix, and constructed based on historical alarm sequences; (2) an alarm relevancy detection strategy is designed to detect and eliminate irrelevant alarms in alarm floods; (3) an online alarm prediction algorithm is designed to predict upcoming alarms at the early stage of the ongoing alarm flood; (4) the confidence intervals of the time differences between the annunciations of subsequent predicted alarm events are calculated for time prediction. To demonstrate the effectiveness of the proposed method, an industrial case study based on real alarm & event logs from an oil refinery is provided.

Do users' characteristics really influence the perceived service quality of Multimodal Transportation Hub (MMTH)? An association rules mining approach

Do users' characteristics really influence the perceived service quality of Multimodal Transportation Hub (MMTH)? An association rules mining approach

Impact of multimorbidity patterns in hospital admissions: the case study of asthma

Background: Most previous studies assessing multimorbidity in asthma assessed the frequency of individual comorbid diseases. Objective: We aimed to assess the frequency and clinical and economic impact of co-occurring groups of comorbidities (comorbidity patterns using the Charlson Comorbidity Index) on asthma hospitalizations. Methods: We assessed the dataset containing a registration of all Portuguese hospitalizations between 2011–2015. We applied three different approaches (regression models, association rule mining, and decision trees) to assess both the frequency and impact of comorbidities patterns in the length-of-stay, in-hospital mortality and hospital charges. For each approach, separate analyses were performed for episodes with asthma as main and as secondary diagnosis. Separate analyses were performed by participants’ age group. Results: We assessed 198340 hospitalizations in patients >18 years old. Both in hospitalizations with asthma as main or secondary diagnosis, combinations of diseases involving cancer, metastasis, cerebrovascular disease, hemiplegia/paraplegia, and liver disease displayed a relevant clinical and economic burden. In hospitalizations having asthma as a secondary diagnosis, we identified several comorbidity patterns involving asthma and associated with increased length-of-stay (average impact of 1.3 [95%CI=0.6–2.0]–3.2 [95%CI=1.8-4.6] additional days), in-hospital mortality (OR range=1.4 [95%CI=1.0–2.0]–7.9 [95%CI=2.6–23.5]) and hospital charges (average additional charges of 351.0 [95%CI=219.1–482.8] to 1470.8 [95%CI=1004.6–1937.0]) Euro compared with hospitalizations without any registered Charlson comorbidity). Consistent results were observed with association rules mining and decision tree approaches. Conclusions: Our findings highlight the importance not only of a complete assessment of patients with asthma, but also of considering the presence of asthma in patients admitted by other diseases, as it may have a relevant impact on clinical and health services outcomes.

Efficient Fuzzy C-means Based Reduced Feature Set Association Rule Mining Approach for Predicting the User Behavioral Pattern in Web Usage Mining

<p>Online business and marketing are becoming popular now a day due to the wide variety of products available from multiple vendors in online. One of the major challenges of e-business merchants is predicting the buying and selling patterns of online customers. Global level competition is another challenge faced by online merchants due to the lowest prices and offers provided by multiple sellers for the same or similar product. Hence, the development of an efficient web mining framework to analyze and predict buyer’s interest based on the browsing history will be a great support to the online sellers by providing exact or relevant product details to the buyers in online. Association rule mining plays an essential role in Web Mining for finding the most frequent and predictive patterns of the user. The major challenge in this approach is the generation of many rules for a huge volume of datasets. Decision making based on association rule mining is critical because knowledge is not directly present in frequent patterns. This research work focuses on the analysis of standard web mining approaches such as k-means clustering, fuzzy c-means clustering, fuzzy k-medoids clustering and fuzzy clustering with weighted session page matrix approach. In this work, MSNBC dataset from UCI Machine Learning Repository has been taken for analysis. Dimensionality reduction plays an important role in the accurate classification of users with respect to their interests. This research work proposed fuzzy C-Means using Kernel Principal Component Analysis (k-PCA) as a dimensionality reduction method based association rule mining classification, grouping and pattern prediction with 100% “confidence” along with a “lift” value greater than 1. The “support” value also shows higher compare with other existing methods and features are effectively reduced in the proposed architecture.</p> <p> </p>

Multivariate analysis of roadway multi-fatality crashes using association rules mining and rules graph structures: A case study in China.

Roadway multi-fatality crashes have always been a vital issue for traffic safety. This study aims to explore the contributory factors and interdependent characteristics of multi-fatality crashes using a novel framework combining association rules mining and rules graph structures. A case study is conducted using data from 1068 severe fatal crashes in China from 2015 to 2020, and 1452 interesting rules are generated using an association rule mining approach. Several modular rules graph structures are constructed based on graph theory to reflect the interactions and patterns between different variables. The results indicate that multi-fatality crashes are highly associated with improper operations, passenger overload, fewer lanes, mountainous terrain, and run-off-the-road crashes, representing the key variables of factors concerning driver, vehicle, road, environment, and accident, respectively. Furthermore, crashes involving different severity levels, road categories, and terrain are verified to possess unique association rules and independent crash patterns. Moreover, the proportion of severe crashes caused by a combination of human-vehicle-road-environment factors (43%) is much higher than that of normal crashes (3%). This study reveals that the hidden associations between various factors contribute to the overrepresentation and severity of multi-fatality crashes. It also demonstrates that the crash mechanisms involving multi-fatality crashes and their interactions are more complex at the system level than those for normal crashes. The proposed framework can effectively map the intrinsic link between multiple crash factors and potential risks, providing transportation agencies with helpful insights for targeted safety measures and preventive strategies.

An incremental rare association rule mining approach with a life cycle tree structure considering time-sensitive data

An incremental rare association rule mining approach with a life cycle tree structure considering time-sensitive data

Intelligent Construction and Mechanism of Educational Big Data Information for Resource Sharing

The word “sharing” is not unfamiliar to everyone now, but many people do not understand the sharing of educational resources (ER), and the distribution of ER does not seem to be the topic that people are most concerned about. This paper is aimed at conducting research on the construction of educational big data information intelligence based on resource sharing. A parallel association rule mining approach for educational big data is proposed in this research. Now, the educational big data is classified, and then, the data is operated. At the same time, a CcG-based educational information resource sharing platform has also been built for educational resource sharing, which can be used for cloud download and upload functions of cloud computing (CcG) for teaching and educational resource sharing. The experimental results of this paper show that the educational information resource sharing platform in this paper processes about 150 information per second, and its service performance must be better than that of traditional educational platforms.