Association Rule Mining Research Articles

Pattern analysis of auto parts failures in the after-sales service network; an interconnected approach of association rules mining and Bayesian networks in the automotive industry

PurposeWarranty-based big data analysis has attracted a great deal of attention because of its key capabilities and role in improving product quality while minimizing costs. Information and details about particular parts (components) repair and replacement during the warranty term, usually stored in the after-sales service database, can be used to solve problems in a variety of sectors. Due to the small number of studies related to the complete analysis of parts failure patterns in the automotive industry in the literature, this paper focuses on discovering and assessing the impact of lesser-studied factors on the failure of auto parts in the warranty period from the after-sales data of an automotive manufacturer.Design/methodology/approachThe interconnected method used in this study for analyzing failure patterns is formed by combining association rules (AR) mining and Bayesian networks (BNs).FindingsThis research utilized AR analysis to extract valuable information from warranty data, exploring the relationship between component failure, time and location. Additionally, BNs were employed to investigate other potential factors influencing component failure, which could not be identified using Association Rules alone. This approach provided a more comprehensive evaluation of the data and valuable insights for decision-making in relevant industries.Originality/valueThis study's findings are believed to be practical in achieving a better dissection and providing a comprehensive package that can be utilized to increase component quality and overcome cross-sectional solutions. The integration of these methods allowed for a wider exploration of potential factors influencing component failure, enhancing the validity and depth of the research findings.

NarmViz: A novel method for visualization of time series numerical association rules for smart agriculture

AbstractNumerical association rule mining (NARM) is a popular method under the umbrella of data mining, focused on finding relationships between attributes in transaction databases. Numerical association rules for time series are a new paradigm that extends the applicability of NARM to the domain of time series. Association rule mining algorithms result in numerous rules, the interpretation of which is sometimes not easy for human experts. Therefore, various visualization methods have been developed to improve the explanation results of the rule mining process. This article is a novel contribution to the development of a new visualization method capable of presenting the association rules for time series developed according to the principles of explainable artificial intelligence. The experiments are conducted in the context of smart agriculture (i.e., agricultural time series data), and show the great potential of the proposed visualization method for the future.

A big data association rule mining based approach for energy building behaviour analysis in an IoT environment

The enormous amount of data generated by sensors and other data sources in modern grid management systems requires new infrastructures, such as IoT (Internet of Things) and Big Data architectures. This, in combination with Data Mining techniques, allows the management and processing of all these heterogeneous massive data in order to discover new insights that can help to reduce the energy consumption of the building. In this paper, we describe a developed methodology for an Internet of Things (IoT) system based on a robust big data architecture. This innovative approach, combined with the power of Spark algorithms, has been proven to uncover rules representing hidden connections and patterns in the data extracted from a building in Bucharest. These uncovered patterns were essential for improving the building’s energy efficiency.

UIoTN‐PMSE: Ubiquitous IoT network‐based predictive modeling in smart environment

SummaryWe proposed a three‐stage intrusion detection system that utilizes a predictive machine learning model to identify and mitigate attacks on ubiquitous network. In the first stage, we applied Apriori‐enabled Association Rule Mining (AARM) feature selection with support vector machine (SVM) for classification of flow of network. Second, we proposed ensemble learning‐based AARM model (PAEL) for behavior analysis. Finally, for classification of multi‐task labels, we proposed swarm bat optimization‐based PAEL model. The trained model is applied to edge and fog computing devices to obtain lower resource utilization and improve the efficiency of the system. The intrusion detection process is performed in three stages: (i) at the edge devices, where abnormal data from network traffic from IoT devices were identified, (ii) the abnormal data sample is sent to fog computing deivce to confirm the attacks and abnormalities, (iii) final identified data sample is sent to cloud server. At cloud, proposed predictive machine learning (ML)‐based generalized weight sum‐enabled ensemble learning (PML‐GWEL) model is trained on sample data, including new detected samples, to continually improve its accuracy. Once the model is trained, it is published to all nodes in the network to update their primary detector models and clear out any outdated pre‐detector models. This process helps to reduce the hardware resources used by the pre‐detector models and improve the overall efficiency of the system. The proposed model is compared with other existing techniques.

Analyzing the relation among different factors leading to Ph.D. dropout using numerical association rule mining

Analyzing the relation among different factors leading to Ph.D. dropout using numerical association rule mining

Digitalization of university education management applicable to association rule mining algorithms

Abstract In this paper, firstly, based on the digital construction of education management in colleges and universities, it is proposed to construct an education management system for colleges and universities to realize the digital development of education management and to design the function and database of the education management system of colleges and universities. Secondly, the Apriori algorithm in association rule mining algorithm is used to define the minimum support and minimum confidence in the variable parameter focus, then the support threshold and confidence threshold are set in the process of optimization connection and pruning, and for the shortcomings of traditional algorithms, the relevance metrics are cited here to improve the systematic data mining efficiency of the Apriori algorithm. Then, the simulation experiment environment and data set are selected, the evaluation index system is determined according to the system requirements, and the education management system based on the association rule mining algorithm is simulated and analyzed. The results show that when min_supp=70%, the Apriori algorithm generates a total of 60 frequent itemsets, and the running time is about 21.2s. Overall, the Apriori algorithm only reads the data set twice, so the program running time is shorter than the Eclat algorithm running time.

Uncovering key molecular mechanisms in the early and late-stage of papillary thyroid carcinoma using association rule mining algorithm.

Thyroid Cancer (TC) is the most frequent endocrine malignancy neoplasm. It is the sixth cause of cancer in women worldwide. The treatment process could be expedited by identifying the controlling molecular mechanisms at the early and late stages, which can contribute to the acceleration of treatment schemes and the improvement of patient survival outcomes. In this work, we study the significant mRNAs through Machine Learning Algorithms in both the early and late stages of Papillary Thyroid Cancer (PTC). During the course of our study, we investigated various methods and techniques to obtain suitable results. The sequence of procedures we followed included organizing data, using nested cross-validation, data cleaning, and normalization at the initial stage. Next, to apply feature selection, a t-test and binary Non-Dominated Sorting Genetic Algorithm II (NSGAII) were chosen to be employed. Later on, during the analysis stage, the discriminative power of the selected features was evaluated using machine learning and deep learning algorithms. Finally, we considered the selected features and utilized Association Rule Mining algorithm to identify the most important ones for improving the decoding of dominant molecular mechanisms in PTC through its early and late stages. The SVM classifier was able to distinguish between early and late-stage categories with an accuracy of 83.5% and an AUC of 0.78 based on the identified mRNAs. The most significant genes associated with the early and late stages of PTC were identified as (e.g., ZNF518B, DTD2, CCAR1) and (e.g., lnc-DNAJB6-7:7, RP11-484D2.3, MSL3P1), respectively. Current study reveals a clear picture of the potential candidate genes that could play a major role not only in the early stage, but also throughout the late one. Hence, the findings could be of help to identify therapeutic targets for more effective PTC drug developments.

Web Based Intrusion Detection System for SQLIA

SQL Injection Attack (SQLIA) refers to an injection attack wherein an attacker can execute malicious SQL statements that control a web application’s database server. By leveraging SQL Injection vulnerability, given the right circumstances, an attacker can use it to bypass a web application’s authentication and authorization mechanisms and retrieve the contents of an entire database. SQL Injection can also be used to add, modify and delete records in a database, affecting data integrity. The main idea of our work is to allow developers the freedom to write and execute code without having to worry about these attacks. In this paper we propose a Web Based Intrusion Detection System for SQLIA to extract a SQL query connecting to database from a PHP file. The structure of the query under observation will be converted to XML file and compared against the legitimate queries stored in the XML file using association rule mining thus minimizing attacks. WEBIDS is expected to reduce the time and manual effort as it only focuses on fragments that are vulnerable for attacks. Key Words: XML Rule Mining, PHP, SQL injection,

Crashes involving distracted pedestrians: Identifying risk factors and their relationships to pedestrian severity levels and distraction modes

The concept of distracted pedestrians and its impact on highway safety has gained increasing attention in recent years. However, studies focusing exclusively on distracted pedestrian crashes are less pervasive than distracted driving. In addition, most prior studies investigate the harmful effect of cellphone usage while walking, without considering other forms of pedestrian distraction. Also, the existing literature provides limited knowledge on comprehending the affinities between pedestrian distraction and safety consequences. This study aims to reveal the chain of contributing factors involved in distracted pedestrian crashes, considering both pedestrian severity levels and specific distraction-related tasks. Ten years (2010–2019) of related crashes were extracted from the Louisiana Department of Transportation and Development (LADOTD) database, and association rule mining (ARM) was applied to identify the meaningful crash patterns. Different distracting activities of pedestrians were introduced from the narratives of police-investigated crash reports. The study findings exhibit the complex nature of distracted pedestrian crashes by highlighting the intricate relationships between risk factors. On road segments, distracted male pedestrians aged 41–64 were more likely to be fatal/severely injured in dark-not-lighted conditions. Crashes involving pedestrians using electronic devices were often found at intersections. Distractions caused by pets, persons, or objects were strongly associated with crossing segments in rural settings. In-person conversation while standing on roadways in urban residential locations without traffic controls was found to increase vulnerability. Working on vehicles while wearing dark clothes and in dark-not-lighted conditions was identified as an influential factor in crash occurrence. Moreover, careless or inattentive actions of pedestrians while playing on the road segments were associated with a high likelihood of crashes. These study outcomes are crucial in uncovering the coexisting crash characteristics related to distracted pedestrians, which can be helpful in targeting and developing effective educational, design, and enforcement strategies to improve pedestrian safety.

Data Mining technique: Application of Apriori algorithm for road accident analysis

Road accidents can happen due to various factors. These factors that contribute to road accidents have cost damage to properties, injuries or deaths and most road accidents are attributable to the lack of knowledge on road safety. To provide safe driving and road safety plans, critical analysis of road accident data is needed, to identify the causes of road accidents. Annually, 1,250,000 people die and 50,000,000 are injured in road accidents worldwide, and fatal road accidents are caused by human error. Improving road conditions is not sufficient, but significantly understanding human errors that cause road accidents, and negligence of corrective and safety driving protocols provided by the concerned government agencies or private organizations. The study aimed to help get insights about the causes of road accidents, and to provide knowledge of road accidents for road safety using Association Rule Mining with the application of the Apriori Algorithm. Association rule mining using the Apriori Algorithm produces significant patterns and insights that help identify the causes of road accidents.

KNOWLEDGE EXTRACTION TECHNIQUES FOR POWER TRANSFORMER MAINTENANCE DATA: REVIEW

The maintenance of power transformers is time or condition-based and at the end of this one, analysis reports are produced to give its status. These different reports produced over time form a large database called the transformer maintenance asset bank. Extracting knowledge from this power transformer maintenance data is now an important subject for the scientific community given the importance of the transformer in the electric power generation chain. The science of data mining finds a field of application for its analysis techniques, the most used in preventive maintenance are predictive techniques. This work reviews knowledge extraction techniques from power transformer maintenance data. For this purpose, 80 articles from a platform are identified and 7 of them are retained at the end after meeting the criteria. Among the predictive analysis techniques namely regression, classification, and prediction, classification is the most used with its ANN (Artificial Neural Network) algorithm. On the other hand, association rule mining (ARM) has the highest accuracy, 98.21% in 2020. In addition, the combination of a classification algorithm preceded by the descriptive one, namely the principal component analysis (PCA), could offer higher accuracy than when they are used individually.

SMART AND INTELLIGENT PRODUCTION STRATEGY FOR THE FLOWER MARKET USING DATA MINING KNOWLEDGE-BASED DECISION

The agriculture industry has been an enormous economic pillar in the production and consumption market value chain. The agriculture industry resets flower production factors with the agricultural technology revolution. The fastest technology provides innovative and intelligent decision-making strategies in seasonal cut flowers to increase production. This study briefs out existing farming practices, chain activity and farming technology’s significant impacts on the agriculture field and garden industry. Authors try to investigate cut flower production status and analyze production values to design innovative and intelligent strategies, especially for seasonal flower production. The study employs a flower dataset; hence, it applies floral parameter inputs and data mining association rules to create an output of the flower production category, which fits appropriately to evaluate flower market production value in a particular season. The article's result reveals that the proposed flower production strategy provides efficient and intelligent guidelines to increase flower production according to market demand. This study suggests an intelligent and friendly production strategy for gardeners that indicates the flower market gets continuous and quality production to meet consumers’ immediate market demand.

Coupled risk analysis of hospital infection: A multimethod-fusion model combining association rules with complex networks

During the initial phase of COVID-19 pandemic, hospital infection (HI) accidents occurred with alarming frequency. Proactively preventing and controlling HI risks is particularly challenging as hospital is a complex and dynamic scene that involves multiple uncertainties and coupled risks. Thus, this paper develops a multimethod fusion model that combines association rule mining with complex networks to systematically analyze coupled HI risks. Firstly, risk factors of HI are obtained and categorized based on real data in hospitals. An improved Apriori algorithm is used to mine coupled relations between risk factors and generate association rules. Then, complex networks of HI risk factors are constructed based on existing rules. Through an in-depth analysis of the networks’ topology and statistics properties, this study reveals that HI risk networks follow a power-law distribution, attesting to the non-random nature of risks. The proposed approach is applied to analyze HI risks during COVID-19 outbreak in a tertiary hospital in Wuhan. Results show that the method can successfully reveal the coupling of risks and identify key risks of HI. Effective responses can be undertaken in advance to prevent identified HI risks, thereby providing valuable guidance for future hospital management.

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.

Discovering and evaluating organizational knowledge from textual data: Application to crisis management

Crisis management effectiveness relies mainly on the quality of the distributed human organization deployed for saving lives, limiting damage and reducing risks. Organizations set up in this context are not always predefined and static; they could evolve and new forms could emerge since actors, such as volunteers or NGO, could join dynamically to collaborate. To improve crisis resolution effectiveness, it is first important to understand, analyze and evaluate such dynamic organizations in order to adjust crisis management plans and ease coordination among actors. Giving a textual experience feedback from past crisis, the objective of this paper is to discover the organizational structure deployed in the considered crisis and then evaluate it according to a set of criteria. For that purpose, we combine in a coherent framework text and association rule mining for pattern discovery and annotation, and multi-agent system models and techniques for formally building and evaluating organizational structures. We present the OSminer algorithm that discovers association rules based on relevant textual patterns and then builds an organizational structure including three main relations between actors: power, control and coordination. A real-life case study, a flood crisis hitting the south west of France, serves as a basis for testing/experimenting our solution. The organizational structure, discovered in this case study, has 24 actors. Its evaluation indicates its efficiency, but shows that it is neither robust nor flexible. Our findings highlight the potential of our approach to discover and evaluate organizational structures from a text recording interactions between stakeholders in a crisis context.

Demand-Oriented Review of a Dynamic Energy-Loss Monitoring System for Primary School Buildings through Micro-Environmental Data Monitoring and Occupant Behavior Analysis

The utilization of primary school buildings is multifaceted, primarily due to the high occupancy density, varying thermal preferences among occupants, diverse indoor activities (such as walking, sports, and conversation), and a constant flow of individuals entering and exiting the building. This results in the frequent opening and closing of external windows and doors and fluctuations in internal heat gain. Consequently, frequent interactions between the indoor and outdoor microenvironments lead to energy losses. This study conducts a comprehensive literature review on building energy loss stemming from occupant behavior and the interactions between indoor and outdoor microenvironments. Furthermore, it proposes a dynamic real-time monitoring system based on a foundation of computer data capture and a visualization platform for building energy loss. The research methods include data crawling, data association rule mining, and data association analysis. The research findings yield a universally applicable and informative building energy-saving design system based on extensive data analysis. Additionally, the system presents information on occupants’ behavior and the microclimate data of indoor and outdoor environments on a computer screen, facilitating human–machine communication and enabling timely adjustments to be made, thus facilitating the construction of design strategies for new buildings and operation and maintenance strategies for existing buildings.

Data Mining in Indian Equity Markets: building low Risk, Market beating Portfolios

Over the last five decades, business academics have identified over 300 determinants that potentially influence stock returns. However, we still do not know whether all return determinants are equally important, or whether there is a smaller set of determinants that has a disproportionately larger influence on stock returns. Can mining historical data help us find this smaller set of return determinants that has a disproportionately higher influence on stock returns? Using historical data from the Indian market, we build a large database of investments with more than 74,000 investments spread over a period of 132 months. From this database, using “association rule mining” method, we are able to mine a strong set of “association rules” that point to a smaller set of “return determinants” that are seen more frequently in investments that beat index returns. From a pool of thirty-seven return determinants, using “association rule mining”, we were able to find out a small set of key return determinants that are seen most frequently in investments that beat index returns in India. Portfolios created from these “association rules” have a portfolio risk lower than the market risk and provide index-beating returns. “Out-of-sample” portfolios created using these association rules have portfolio “Beta” less than one and provide returns that beat the market returns by a significant margin for all holding periods in the Indian market. Through this paper, we demonstrate how portfolio managers can mine “association rules” and build portfolios without any limits on the number of factors that can be included in the screening process.

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.

An Optimized Association Rules Mining Framework for Chinese Social Insurance Fund Data Auditing

Association rules mining with the Chinese social insurance fund dataset can effectively discover different kinds of errors, irregularities, and illegal acts by providing auditors with relationships among the items and therefore improve auditing quality and efficiency. However, traditional positive and negative association rules (PNARs) mining algorithms inevitably produce too many meaningless or contradictory rules when these two types of rules are mined simultaneously, which brings a huge challenge to auditors retrieving decision information. Aimed to reduce the quantity of low-reliability PNARs without missing interesting rules, this paper first proposes an improved PNARs mining algorithm with minimum correlation and triple confidence threshold to control the mined rules number by narrowing the range of confidence settings. Then, a novel pruning algorithm based on the inclusion relation of the rule’s antecedent and consequent is given to remove those redundant rules. After that, the proposed optimized PNARs mining approach is applied to the Chinese social insurance fund dataset starting with audit features influence factors mining using the Hash table. The experimental results with different datasets show that the proposed framework not only can ensure effective and interesting rules extraction but also has better performance than traditional approaches in both accuracy and efficiency, reducing the number of redundant PNARs by over 70.1% with experimental datasets and average 78.5% with auditing data mining, respectively.

Prescription pattern analysis of Type 2 Diabetes Mellitus: a cross-sectional study in Isfahan, Iran

BackgroundPatients with Type 2 Diabetes Mellitus (T2DM) are at a higher risk of polypharmacy and more susceptible to irrational prescriptions; therefore, pharmacological therapy patterns are important to be monitored. The primary objective of this study was to highlight current prescription patterns in T2DM patients and compare them with existing Standards of Medical Care in Diabetes. The second objective was to analyze whether age and gender affect prescription patterns.MethodThis cross-sectional study was conducted using the Iran Health Insurance Organization (IHIO) prescription database. It was mined by an Association Rule Mining (ARM) technique, FP-Growth, in order to find co-prescribed drugs with anti-diabetic medications. The algorithm was implemented at different levels of the Anatomical Therapeutic Chemical (ATC) classification system, which assigns different codes to drugs based on their anatomy, pharmacological, therapeutic, and chemical properties to provide an in-depth analysis of co-prescription patterns.ResultsAltogether, the prescriptions of 914,652 patients were analyzed, of whom 91,505 were found to have diabetes. According to our results, prescribing Lipid Modifying Agents (C10) (56.3%), Agents Acting on The Renin-Angiotensin System (C09) (48.9%), Antithrombotic Agents (B01) (35.7%), and Beta Blocking Agents (C07) (30.1%) were meaningfully associated with the prescription of Drugs Used in Diabetes. Our study also revealed that female diabetic patients have a higher lift for taking Thyroid Preparations, and the older the patients were, the more they were prone to take neuropathy-related medications. Additionally, the results suggest that there are gender differences in the association between aspirin and diabetes drugs, with the differences becoming less pronounced in old age.ConclusionsAlmost all of the association rules found in this research were clinically meaningful, proving the potential of ARM for co-prescription pattern discovery. Moreover, implementing level-based ARM was effective in detecting difficult-to-spot rules. Additionally, the majority of drugs prescribed by physicians were consistent with the Standards of Medical Care in Diabetes.