Applying Association Rule Mining Research Articles

Discovering hidden patterns: Association rules for cardiovascular diseases in type 2 diabetes mellitus.

It is increasingly common to find patients affected by a combination of type 2 diabetes mellitus (T2DM) and coronary artery disease (CAD), and studies are able to correlate their relationships with available biological and clinical evidence. The aim of the current study was to apply association rule mining (ARM) to discover whether there are consistent patterns of clinical features relevant to these diseases. ARM leverages clinical and laboratory data to the meaningful patterns for diabetic CAD by harnessing the power help of data-driven algorithms to optimise the decision-making in patient care. To reinforce the evidence of the T2DM-CAD interplay and demonstrate the ability of ARM to provide new insights into multivariate pattern discovery. This cross-sectional study was conducted at the Department of Biochemistry in a specialized tertiary care centre in Delhi, involving a total of 300 consented subjects categorized into three groups: CAD with diabetes, CAD without diabetes, and healthy controls, with 100 subjects in each group. The participants were enrolled from the Cardiology IPD & OPD for the sample collection. The study employed ARM technique to extract the meaningful patterns and relationships from the clinical data with its original value. The clinical dataset comprised 35 attributes from enrolled subjects. The analysis produced rules with a maximum branching factor of 4 and a rule length of 5, necessitating a 1% probability increase for enhancement. Prominent patterns emerged, highlighting strong links between health indicators and diabetes likelihood, particularly elevated HbA1C and random blood sugar levels. The ARM technique identified individuals with a random blood sugar level > 175 and HbA1C > 6.6 are likely in the "CAD-with-diabetes" group, offering valuable insights into health indicators and influencing factors on disease outcomes. The application of this method holds promise for healthcare practitioners to offer valuable insights for enhancing patient treatment targeting specific subtypes of CAD with diabetes. Implying artificial intelligence techniques with medical data, we have shown the potential for personalized healthcare and the development of user-friendly applications aimed at improving cardiovascular health outcomes for this high-risk population to optimise the decision-making in patient care.

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.

Understanding distracted driving patterns of ride-hailing drivers from multi-source data: Applying association rule mining

This study aims to explore the association between distracted driving (including cognitive, visual, operational, and auditory distractions) and multi-source features of ride-hailing drivers, and to discover frequent patterns of distracted driving. To achieve this, a natural driving experiment was conducted, and an association rule mining (‘Apriori’ algorithm) was used to uncover hidden rules between distracted driving and the multi-source features (including emotion, valence, arousal, driving tasks, cumulative driving hours, velocity, longitudinal acceleration, lateral acceleration, heading rate, presence of intersections, traffic status, and driving time of day). Results indicate that distracted driving is prevalent among ride-hailing drivers in specific scenarios, including trips without passengers, non-intersection sections, driving for over 4 h, and congested traffic conditions. The emotional state of drivers has also been found to have an interesting association with distracted driving. For instance, cognitive and operational distraction were highly associated with positive driving emotions, but auditory distraction caused by passenger interference was highly associated with negative driving emotions. Moreover, there are variations in distracted driving patterns across different categories. These findings can help ride-hailing platforms develop more scientific and effective distracted driving monitoring and prevention strategies.

Applying Association Rule Mining to Explore Unsafe Behaviors in the Indonesian Construction Industry

The frequency of work accidents in construction projects is relatively high. One contributing factor to work accidents is unsafe behavior by workers at construction sites. In Indonesia, this is the first study to investigate 2503 instances of unsafe behavior that occurred across Indonesian construction projects in relation to their attributes to obtain insightful knowledge by using the association rule mining (ARM) method. Association rule mining was used to explore the database. As a result, two consolidated rules were obtained. The most frequent unsafe behaviors were workers putting tools and materials in random places, workers not attaching safety lines at provided places, and workers moving work tools and materials in ways that were not in accordance with procedures. These unsafe behaviors were associated with accident types of falling, and being struck or cut by items, as well as violations of Manpower and Transmigration Ministerial Regulation 01/1980, and Manpower Ministerial Regulation 09/2016. The ARM results were evaluated with a reliability evaluation method before being validated by construction safety experts. Hence, the findings are reliable to be used as guideline information for safety trainers to prioritize related safety trainings and for safety inspectors when carrying out inspections on construction sites. As a result, safety management and safety performance can increase significantly.

Explainable Integration of Social Media Background in a Dynamic Neural Recommender

Recommender systems nowadays are commonly deployed in e-commerce platforms to help customers making purchase decisions. Dynamic recommender considers not only static user-item interaction data, but the temporal information at the time of recommendation. Previous researches have suggested to incorporate social media as the temporal information in dynamic neural recommenders after transforming them into embeddings. While such an approach can potentially improve recommendation performance, the effectiveness is difficult to explain. In this article, we propose an explainable method to integrate social media in a dynamic neural recommender. Our method applies association rule mining, which can generate human-understandable behavior patterns from social media and e-commerce platforms. With real-world social media and e-commerce data, we show that the integration can improve accuracy by up to 14% while using the same data. Moreover, we can explain the positive cases by examining relevant association rules.

On the impact of advanced driver assistance systems on driving distraction and risky behaviour: An empirical analysis of irish commercial drivers

Advanced driver assistance systems (ADAS) present promising benefits in mitigating road collisions. However, these benefits are limited when risky drivers continue engaging in distraction events. While there is evidence that real-time warnings help improve driving behaviour, the sustained benefits of warning-based ADAS on reducing driving distraction in light commercial vehicle (LCV) drivers remain unclear. This research determines the effect of receiving instant distraction warnings over two years using a naturalistic driving dataset comprising around one million trips from 373 LCV drivers in the Republic of Ireland. Furthermore, the study applies Association Rule Mining (ARM) to find the contextual variables (e.g., speed limit, road type, traffic conditions) that increase the likelihood of distraction events. The results show that warning-based ADAS providing real-time warnings helps reduce distraction events triggering driver inattention, forward collision, and lane departure warnings. Over half of the studied fleet reduced these warnings by at least 50% – lane departure after two months and driver inattention and forward collision after six months. It is found that both passive and active monitoring systems, coupled with coaching and rewards, significantly reduce aggressive driving behaviours tied to harsh acceleration (by 76%) and harsh braking (by 65%). The results of ARM show that the driving context introduces explanatory information for road safety programs. Low-speed urban roads and the summer season increase the likelihood of driver inattention and forward collision warnings. In contrast, high-speed rural roads increase the likelihood of lane departure warnings. These research findings support road safety stakeholders in developing risk assessments based on warning-based ADAS, targeted campaigns to reduce driving distraction, and driving coaching programs.

Comorbidity Patterns of Mood Disorders in Adult Inpatients: Applying Association Rule Mining.

This study explored physical and psychiatric comorbidities of mood disorders using association rule mining. There were 7709 subjects who were patients (19 years old) diagnosed with mood disorders and included in the data collected by the Korean National Hospital Discharge In-depth Injury Survey (KNHDS) between 2006 and 2018. Physical comorbidities (46.17%) were higher than that of psychiatric comorbidities (27.28%). The frequent comorbidities of mood disorders (F30–F39) were hypertensive diseases (I10–I15), neurotic, stress-related and somatoform disorders (F40–F48), diabetes mellitus (E10–E14), and diseases of esophagus, stomach, and duodenum (K20–K31). The bidirectional association path of mood disorders (F30–F39) with hypertensive diseases (I10–I15) and diabetes mellitus (E10–E14) were the strongest. Depressive episodes (F32) and recurrent depressive disorders (F33) revealed strong bidirectional association paths with other degenerative diseases of the nervous system (G30-G32) and organic, including symptomatic and mental disorders (F00–F09). Bipolar affective disorders (F31) revealed strong bidirectional association paths with diabetes mellitus (E10–E14) and hypertensive diseases (I10–I15). It was found that different physical and psychiatric disorders are comorbid according to the sub-classification of mood disorders. Understanding the comorbidity patterns of major comorbidities for each mood disorder can assist mental health providers in treating and managing patients with mood disorders.

Chiller system performance management with market basket analysis

PurposeThis study aims to apply association rule mining (ARM) to uncover specific associations between operating components of a chiller system and improve its coefficient of performance (COP), hence reducing the electricity use of buildings with central air conditioning.Design/methodology/approachFirst, 13 operating variables were identified, comprising measures of temperatures and flow rates of system components and their switching statuses. The variables were grouped into four bins before carrying out ARM. Strong rules were produced to associate the variables and switching statuses with different COP classes.FindingsThe strong rules explain existing constraints on practising chiller sequencing and prioritise variables for optimisation. Based on strong rules for the highest COP class, the optimal operating strategy involves rescheduling chillers and their associated components in pairs during a high load operation. Resetting the chilled water supply temperature is the next best strategy, followed by resetting the condenser water entering temperature, subject to operating constraints.Research limitations/implicationsThis study considers the even frequency method with four bins only. Replication work can be done with other discretisation methods and different numbers of classes to compare potential differences in the bin ranges of the optimised variables.Practical implicationsThe strong rules identified by ARM highlight associations between variables and high or low COPs. This supports the selection of critical variables and the operating status of system components to maximise the COP. Tailor-made optimisation strategies and the associated electricity savings can be further evaluated.Originality/valuePrevious studies applied ARM for chiller fault detection but without considering system performance under the interaction of different components. The novelty of this study is its demonstration of ARM’s intelligence at discovering associations in past operating data. This enables the identification of tailor-made energy management opportunities, which are essential for all engineering systems. ARM is free from the prediction errors of typical regression and black-box models.

Analyzing Customer Satisfaction Over Telecom Companies in Sudan Using Apriori Algorithm

With the rapid growth in the field of information and communication technology, the demand for telecommunications and internet services increased over the years. In Sudan, three reputed telecom companies provide these types of services. Although a great competition between these companies in order to attract more new customers and also to keep the existing customers satisfied, many new and existing customers still has some apprehensions over the difficulty on how to choose the best among the service providers in terms of full range of services with affordable cost. This study aims to measure the customer satisfaction from the telecom companies in Sudan. The survey questionnaire designed and distributed electronically to as many customers to find frequent patterns by applying association rule mining techniques. The results of analysis show that Zain Sudan performs better compared to MTN and Sudani telecom companies in terms of various customer-oriented services.

Using association rule mining to jointly detect clinical features and differentially expressed genes related to chronic inflammatory diseases.

It is increasingly common to find patients affected by a combination of type 2 diabetes mellitus (T2DM), dyslipidemia (DLP) and periodontitis (PD), which are chronic inflammatory diseases. More studies able to capture unknown relationships among these diseases will contribute to raise biological and clinical evidence. The aim of this study was to apply association rule mining (ARM) to discover whether there are consistent patterns of clinical features (CFs) and differentially expressed genes (DEGs) relevant to these diseases. We intend to reinforce the evidence of the T2DM-DLP-PD-interplay and demonstrate the ARM ability to provide new insights into multivariate pattern discovery. We utilized 29 clinical glycemic, lipid and periodontal parameters from 143 patients divided into five groups based upon diabetic, dyslipidemic and periodontal conditions (including a healthy-control group). At least 5 patients from each group were selected to assess the transcriptome by microarray. ARM was utilized to assess relevant association rules considering: (i) only CFs; and (ii) CFs+DEGs, such that the identified DEGs, specific to each group of patients, were submitted to gene expression validation by quantitative polymerase chain reaction (qPCR). We obtained 78 CF-rules and 161 CF+DEG-rules. Based on their clinical significance, Periodontists and Geneticist experts selected 11 CF-rules, and 5 CF+DEG-rules. From the five DEGs prospected by the rules, four of them were validated by qPCR as significantly different from the control group; and two of them validated the previous microarray findings. ARM was a powerful data analysis technique to identify multivariate patterns involving clinical and molecular profiles of patients affected by specific pathological panels. ARM proved to be an effective mining approach to analyze gene expression with the advantage of including patient's CFs. A combination of CFs and DEGs might be employed in modeling the patient's chance to develop complex diseases, such as those studied here.

Cluster Prediction Model for Market Basket Analysis: Quest for Better Alternatives to Associative Rule Mining Approach

Market basket analysis seeks to apply association rule mining on the massive sales transaction data. It yields an outcome that either aims to suppress product stock-up unnecessarily and/or product being stock-out. Such decision support system seeks to avoid the unnecessary demurrage and help businesses to keep their customers via better decision and improved service. Market data are time-bound on supply-demand value chain. With customer behavior varying in time, we seek to predict purchase of commonly combined itemset for a next period – so that businesses can better support their decisions via adequate provisions of the required inventory. We use 3-KDD dataset and Delta Mall dataset – adapting a time-clustering algorithm that examines buying behavior of customers, their preferences and frequency with which goods are purchased in common as a basket. Model yields average 162-rules for four-dataset from dataset. Result shows that previous basket items by random customers allow the selection purchase of items of similar value as best combined due to its shelf-placement using the concept of feature drift.

Applying Association Rule Mining to Analyze the Performance of Indian Cricket Team in T20 Format

This paper discusses the application of Association Rule Mining for evaluating the performance of Indian Cricket Team specifically in the fastpaced T20 format. Data for the study has been obtained from multiple authentic secondary sources. Further, Indian Team’s performance in the said format is studied and association between relevant data points in cricket such as result of toss, home ground or away ground, order of innings, setting a target or chasing a target, match result has been considered. Knowledge thus gained from the rules aims at helping the stakeholders in devising a match winning approach. The results of the study indicate Indian Team’s upper hand in the T20 format and provides further insights into conditions in which India is likely to perform even better.

Detection of Sentiment Analysis with Co-Occurrence Data using Supervised and Unsupervised Methods

With the rapid growth of user-generated content on the internet, sentiment analysis of online reviews has become a hot research topic recently, but due to variety and wide range of products and services, the supervised and unsupervised domain- specific models are often not practical. As the number of reviews expands, it is essential to develop an efficient sentiment analysis model that is capable of extracting product aspects and determining the sentiments for aspects. A text processing framework that can summarize reviews would therefore be desirable. A subtask to be performed by such a framework would be to find the general aspect categories addressed in review sentences, for which this paper presents two methods. In this paper, we propose an unsupervised model for detecting aspects in reviews. In this model, first a generalized method is proposed to learn multi-word aspects. Second, a set of heuristic rules is employed to take into account the influence of in opinion word ion detecting the aspect. In contrast to most existing approaches, the first method presented is an unsupervised method that applies association rule mining on co-occurrence frequency data obtained from a corpus to find these aspect categories. The proposed unsupervised method performs better than several simple baselines, a similar but supervised method, and a supervised baseline; the proposed model does not require labeled training data and can be applicable to other languages or domains. We demonstrate the effectiveness of our model on a collection of product reviews dataset, where it outperforms other techniques.

Development of a FRAM-based framework to identify hazards in a complex system

System-based approaches such as Functional Resonance Analysis Model (FRAM) are developed to model the complex interactions of system variables and their performance variabilities that may lead to a hazardous scenario in a complex system. However, they have limitations to be applied in process industries for hazard identification since they are heavily based on qualitative analysis and expert elicitations. To overcome the limitations of the system-based hazard identification, the study developed a FRAM-based framework to integrate a human performance model, an equipment performance model, and a first-principle based chemical process model into a hybrid simulator, which will be able to aid hazard analysis in the process industries. The simulator is capable of simulating the performance variabilities of the functions through the aggregation of mathematical models within a complex system, which can be used to simulate potential hazard situations and identify the corresponding interactions. Interaction analysis is conducted by applying association rule mining to the simulated data. The impact of the interactions among upstream functions on the performance of downstream functions can be identified by interpreting the rules, whose antecedents contain upstream functions and consequents contain downstream functions.

Emotions Identification by Using Unsupervised Aspect Category Based Sentiment Classification

The social media is growing at an astonishing rate; this has resulted in increased online communications. The online communication contains feedbacks, comments, and reviews that are posted on the internet by users. To analyze such data, the paper represents the Aspect-based unsupervised method that applies association rule mining on customer reviews aims to algorithmically identify product aspects, their corresponding opinions from a collection of opinionated reviews. This framework involves four main subtasks: Product aspect identification, Sentiment expression identification, Emotion Detection, Comparison of Products. This paper also represents a Comparative study of sentiment analysis techniques including machine learning technique and lexicon based technique. The comparisons are majorly drawn based on features such as techniques, data source, data scope, and limitations. The proposed framework performs well with F1-Score 76.426%.

Study of Attention-Deficit Hyperactivity Disorder (ADHD) in Children- Applying Association Rule Mining (ARM) (Survey)

Study of Attention-Deficit Hyperactivity Disorder (ADHD) in Children- Applying Association Rule Mining (ARM) (Survey)

Too trivial to test? An inverse view on defect prediction to identify methods with low fault risk.

BackgroundTest resources are usually limited and therefore it is often not possible to completely test an application before a release. To cope with the problem of scarce resources, development teams can apply defect prediction to identify fault-prone code regions. However, defect prediction tends to low precision in cross-project prediction scenarios.AimsWe take an inverse view on defect prediction and aim to identify methods that can be deferred when testing because they contain hardly any faults due to their code being “trivial”. We expect that characteristics of such methods might be project-independent, so that our approach could improve cross-project predictions.MethodWe compute code metrics and apply association rule mining to create rules for identifying methods with low fault risk (LFR). We conduct an empirical study to assess our approach with six Java open-source projects containing precise fault data at the method level.ResultsOur results show that inverse defect prediction can identify approx. 32–44% of the methods of a project to have a LFR; on average, they are about six times less likely to contain a fault than other methods. In cross-project predictions with larger, more diversified training sets, identified methods are even 11 times less likely to contain a fault.ConclusionsInverse defect prediction supports the efficient allocation of test resources by identifying methods that can be treated with less priority in testing activities and is well applicable in cross-project prediction scenarios.

Understanding the Purchase Behaviour of Turkish Consumers in B2C E-Commerce

The significant improvements in communication technologies revealed innovative online services. Time and location barriers have been eliminated and customer satisfaction has gained importance. The most remarkable evolution is observed in electronic commerce. Electronic commerce is a widely accepted industry which provides an effective medium for both retailers and customers, enabling to perform online transactions through websites. The analysis of consumer preferences has become a key aspect in this challenging environment. In this study, the behaviours of Turkish consumers were identified to provide an insight to the practitioners. We examined the moderator effect of consumers' socio-demographic characteristics on online purchasing behaviours. A questionnaire was designed by using the existing literature. A data set of 337 valid records were collected by a questionnaire and analysed by applying association rule mining method. Several interesting rules representing the preferences of online consumers were determined. The results show that the behaviours of Turkish online consumers are related with their demographic characteristics.

Impact of ICT on Students’ Academic Performance: Applying Association Rule Mining and Structured Equation Modeling

Information and communication technology (ICT) plays a significant role in university students’ academic performance. This research examined the effect of ICT on the students’ academic performance at different private universities in Chittagong, Bangladesh. Primary data have been collected from the students of those universities using a survey questionnaire. Descriptive Statistics, Reliability Analysis, Confirmatory Factor Analysis, OLS regression, Structured Equation Modeling (SEM) and Data Mining algorithms such as Association rule mining and éclat have been employed to evaluate the comparative importance of the factors in identifying the academic performance of the students. From a statistical and mining perspective, overall results indicate that there is a significant relationship between ICT use and students’ academic performance. Also, student’s addiction to ICT has a significant influence on the comparative measurement in identifying the academic performance of the students. Finally, some recommendations are provided on the basis of the findings.

Using Data Mining Techniques to Guide Academic Programs Design and Assessment

This paper presents a novel application of data mining techniques to guide academic programs design and assessment. More specifically, it propose using association rule mining techniques to discover a set of rules that govern the relationship between two core components of an academic program, program educational objectives (PEOs) and students outcomes(SOs). As a case study, this paper demonstrates how association rule mining techniques are applied to mine mapping rules between the PEOs and a predefined set of SOs adopted by the American Board for Engineering and Technology-Engineering Accreditation Commission (ABET-EAC) for engineering programs. To this end, a set of 152 ABET-EAC accredited engineering programs’ self-study reports have been collected and the mapping data between their PEOs and ABET-EAC SOs have been extracted. This dataset has been pre-processed and transformed into a representation suitable for applying association rule mining techniques. This involves identifying a set of PEOs labels, annotating data instances with PEOs labels, and projecting each multi-label data instance into a set of single-label instances. Apriopi algorithm is then applied to discover the rules that govern the mapping between PEOs and ABET-EAC SOs. The discovered rules are of particular importance for guiding the design and assessment of engineering academic programs. Besides that, the discovered rules unveil of a number of interesting correlations between PEOs and ABET-EAC SOs that need further investigation by pedagogists.