Association Rule Mining Technique Research Articles

The Discovery of Oral Cancer Prognostic Factor Ranking Using Association Rule Mining.

A 5-year survival rate is a predictor for the assessment of oral cancer prognosis. The purpose of this study is to analyze oral cancer data to discover and rank the prognostic factors associated with oral cancer 5-year survival using the association rule mining (ARM) technique. This study is a retrospective analysis of 897 oral cancer patients from a regional cancer center between 2011 and 2017. The 5-year survival rate was assessed. The multivariable Cox proportional hazards analysis was performed to determine prognostic factors. ARM was applied to clinicopathologic and treatment modalities data to identify and rank the prognostic factors associated with oral cancer 5-year survival. The 5-year overall survival rate was 35.1%. Multivariable Cox proportional hazards analysis showed that tumor (T) stage, lymph node metastasis, surgical margin, extranodal extension, recurrence, and distant metastasis of tumor were significantly associated with overall survival rate (p < 0.05). The top associated death within 5 years rule was positive extranodal extension, followed by positive perineural and lymphovascular invasion, with confidence levels of 0.808, 0.808, and 0.804, respectively. This study has shown that extranodal extension, and perineural and lymphovascular invasion were the top ranking and major deadly prognostic factors affecting the 5-year survival of oral cancer.

Data driven approaches using association rule mining (ARM) techniques to elaborate the role of genetic alteration in difficult epilepsy

Data driven approaches using association rule mining (ARM) techniques to elaborate the role of genetic alteration in difficult epilepsy

Interaction mechanisms of interface management risks in complex systems of high-speed rail construction projects: an association rule mining-based modeling framework

PurposeThis study aims to develop an interface management risk interaction modeling and analysis methodology applicable to complex systems in high-speed rail construction projects, reveal the interaction mechanism of interface management risk and provide theoretical support for project managers to develop appropriate interface management risk response strategies.Design/methodology/approachThis paper introduces the association rule mining technique to improve the complex network modeling method. Taking China as an example, based on the stakeholder perspective, the risk factors and significant accident types of interface management of high-speed rail construction projects are systematically identified, and a database is established. Then, the Apriori algorithm is used to mine and analyze the strong association rules among the factors in the database, construct the complex network, and analyze its topological characteristics to reveal the interaction mechanism of the interface management risk of high-speed rail construction projects.FindingsThe results show that the network is both scale-free and small-world, implying that construction accidents are not random events but rather the result of strong interactions between numerous interface management risks. Contractors, technical interfaces, mechanical equipment, and environmental factors are the primary direct causal factors of accidents, while owners and designers are essential indirect causal factors. The global importance of stakeholders such as owners, designers, and supervisors rises significantly after considering the indirect correlations between factors. This theoretically explains the need to consider the interactions between interface management risks.Originality/valueThe interaction mechanism between interface management risks is unclear, which is an essential factor influencing the decision of risk response measures. This study proposes a new methodology for analyzing interface management risk response strategies that incorporate quantitative analysis methods and considers the interaction of interface management risks.

A data-knowledge-driven interval type-2 fuzzy neural network with interpretability and self-adaptive structure

Interval type-2 fuzzy neural networks (IT2FNNs) have gained sustainable attention and wide applications because of their power of adaptive fuzzy modeling. Although the existing models accurately simulate the relationships between inputs and outputs by various adaptive learning algorithms, the parameters and fuzzy rules characterizing the model behaviors no longer have explicit interpretations. To achieve good interpretability and generalization performance, this paper proposes a novel data-knowledge-driven IT2FNN (DKIT2FNN). Unlike accuracy-oriented model fitting, DKIT2FNN pioneers the use of famous association rule mining techniques to automatically explore the interpretable rule knowledge, which intuitively presents conditional relation patterns among features. Using the obtained knowledge, the DKIT2FNN model is accomplished in a single-hidden layer network framework, in which the antecedent structure of each fuzzy rule is determined by the relation patterns provided by rule knowledge. In addition, an adversarial incremental learning algorithm is proposed for the self-organizing modeling of DKIT2FNN. Besides automatically assembling a compact rule base, it integrates the label adversarial training to reinforce the model's capability against label noise. We validate the DKIT2FNN on classification benchmark datasets with label noise and compare it with state-of-the-art non-fuzzy and neuro-fuzzy methods. The results demonstrate that the proposed DKIT2FNN achieves robust classification performance and good interpretability.

Urban Traffic Accident Features Investigation to Improve Urban Transportation Infrastructure Sustainability by Integrating GIS and Data Mining Techniques

Urban traffic accidents pose significant challenges to the sustainability of transportation infrastructure not only in Vietnam but also all over the world. To decrease the frequency of accidents, it is crucial to analyze accident data to determine the relationship between accidents and causes, especially for serious accidents. This study suggests an integrated approach using Geographic Information System (GIS) and Data Mining methods to investigate the features of urban traffic accidents in Hanoi, Vietnam aiming to solve these challenges and enhance the safety and efficiency of urban transportation. Firstly, the dataset was segmented into homogenous clusters using the two-step cluster method. Secondly, the correlation between causes and traffic accidents was examined on the overall dataset as well as on each cluster using the association rule mining (ARM) technique. Finally, the location of accident groups and high-frequency sites of accidents (hotspots) were determined by using GIS techniques. As a result, a five-cluster model was created, which corresponded to five common accident groupings in Hanoi. Moreover, the results of the study also identified the types of accidents, the main causes, the time, and the surrounding areas corresponding to each accident group. In detail, cluster 5 depicted accidents on streets, provincial, and national roads caused by motorbikes making up the highest percentage within the groups, accounting for 29.2%. Speeding and driving in the wrong lane in the afternoon and at night were the main causes in this cluster (Cf ≥ 0.9 and Lt ≥ 1.22). Next, cluster 2 had the second-highest proportion. Cluster 2 presented accidents between a truck/car and a motorbike on national and provincial roads, accounting for 27.8%. Cluster 1 presented accidents between a truck/car and a motorbike on local streets, accounting for 22%. Cluster 3 illustrated accidents between two motorbikes on the country lanes, accounting for 12.3%. Finally, cluster 4 depicted single-vehicle motorbike crashes, with the lowest rate of 8.8%. More importantly, this study also recommended using repeatability criteria for the same type of accidents or causes to determine the location of hotspots. Also, suggestions for improving traffic infrastructure sustainability were proposed. To our knowledge, this is the first time in which these three methods are applied simultaneously for analyzing traffic accidents.

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.

Investigating the effect of inflation on the consumption pattern of Iranian households

The big data sources of National Statistical Offices (NSOs) are provided to make a superior platform for decision-making. The household income and expenditure survey is one of the economically important surveys especially when the inflation rate varies to assess the changes in households’ consumption patterns. In this case, big data can be beneficial and help to accurately measure consumption patterns of urban and rural households at every geographical level. This analysis is an exploratory study for the extraction of the size of injustice and imparity of household income and facilities implemented by classifying and clustering all Iranian households. Through this study, classification and soft clustering (Fuzzy clustering) techniques are employed to characterize the Iranian household types from 2011 to 2021, which are supervised and unsupervised approaches, respectively. Moreover, association rule mining techniques are employed to discover and extract consumption patterns for each cluster. Obtained results showed that there was a significant gap between purchasing power/receiving energy between lowest and highest income households from 2011 to 2021, and this gap is increasing day by day.

Early-stage pregnancy recognition on microblogs: Machine learning and lexicon-based approaches

Pregnancy carries high medical and psychosocial risks that could lead pregnant women to experience serious health consequences. Providing protective measures for pregnant women is one of the critical tasks during the pregnancy period. This study proposes an emotion-based mechanism to detect the early stage of pregnancy using real-time data from Twitter. Pregnancy-related emotions (e.g., anger, fear, sadness, joy, and surprise) and polarity (positive and negative) were extracted from users' tweets using NRC Affect Intensity Lexicon and SentiStrength techniques. Then, pregnancy-related terms were extracted and mapped with pregnancy-related sentiments using part-of-speech tagging and association rules mining techniques. The results showed that pregnancy tweets contained high positivity, as well as significant amounts of joy, sadness, and fear. The classification results demonstrated the possibility of using users’ sentiments for early-stage pregnancy recognition on microblogs. The proposed mechanism offers valuable insights to healthcare decision-makers, allowing them to develop a comprehensive understanding of users' health status based on social media posts.

The Effect of Information Exchange Activities on Literacy in Online Health Community: The Evidence from PatientsLikeMe

Online health communities (OHC) consist of individuals with shared health-related interests who exchange health-related information among themselves and for the benefit of others. Unfortunately, a notable issue within these communities is the dissemination of a substantial volume of inaccurate health information by various online health groups. Nevertheless, a dearth of research examining the impact of information-seeking activities within OHCs exists. This study aimed to examine the influence of direct and indirect health information-seeking behaviors, specifically among users diagnosed with Type 2 diabetes who have reported complications in OHC, also called claims. Employing association rule mining (ARM) techniques, user data from PatientsLikeMe were extracted to capture information on users’ reported complications subsequent to being diagnosed with Type 2 diabetes (N = 6371). Subsequently, we utilized zero-inflated negative binomial regression (ZINB) to evaluate the effect of direct and indirect information search activities on false notes, including their interaction of them. The outcomes of this investigation have the potential to offer patients valuable insights regarding the reliability and trustworthiness of information derived from OHCs.

Unveiling Thyroid Disease Associations: An Exceptionality-Based Data Mining Technique

Background: The prevalence of thyroid disease has seen a rapid increase in recent times, primarily attributed to the fast pace of lifestyles that often result in poor dietary choices, work-life imbalances, social stress, genetic mutations, and improved diagnostic capabilities. However, the precise contribution of these factors to thyroid disease remains a subject of controversy. Consequently, there is a pressing need to gain a comprehensive understanding of the related associations in order to potentially mitigate the associated morbidity and mortality rates. Methods: This study employed association rule mining techniques to reveal hidden correlations among complex and diverse epidemiological connections pertaining to thyroid disease associations. We proposed a framework which incorporates text mining and association rule mining algorithms with exceptionality measurement to simultaneously identify common and exception risk factors correlated with the disease through real-life digital health records. Two distinctive datasets were analyzed through two algorithms, and mutual factors were retained for interpretation. Results: The results confirmed that age, gender, and history of thyroid disease are risk factors positively related to subsequent thyroid cancer. Furthermore, it was observed that the absence of underlying chronic disease conditions, such as diabetes, hypertension, or obesity, are associated with reduced likelihood of being diagnosed with thyroid cancer. Conclusions: Collectively, the proposed framework demonstrates its sound feasibility and should be further recommended for different disease in-depth knowledge discovery.

Data-mining-based identification of post-handover defect association rules in apartment housings

Abstract With the increasing expectations of clients and the growing complexity of the built environment, property management teams are facing constant pressure to effectively manage and rectify defects for improved building operational efficiency and performance. This study aims to develop and validate a defect correlation evaluation model for project and property management professionals by specifically (i) examining the defect detection and management mechanisms of residential buildings and (ii) quantifying the mechanical characteristics of defects by using association rules mining (ARM) techniques. In addressing the limitations of current evaluation approaches, this study proposed an ARM evaluation model that integrated, contextualized, and operationalized building defects into work type, location, elements, and defect type. The association between these classifications was explored and mapped. Among the resulting 123 meaningful rules, rules occurred at a rate of about 62% of the same work type in the linked work type, nearly 193% of the same element in another element, and about 23% of the close location in the far location. In conclusion, this study informs project and property management professionals of the key and complex associations between defects of different characteristics and highlights the most common occurrence defects in residential apartment buildings. Thus, this helps reduce the ambiguity and subjectivity of prioritization in defect management and facilitates maintenance and repair planning.

Haptic technology in society: A sentiment analysis of public engagement

With the rapid rise of metaverse spaces, the integration of haptic technology has become an integral part of users' immersion and engagement in different domains. This study shades the light on public engagement with haptic technology applications in various contexts. The YouTube Application Programming Interface was used to extract data about haptic technology within a specific time period. Public engagement was estimated based on users' cognitive and behavioral engagement with haptic-video materials. A topic modeling approach was used to extract the main topics associated with haptic technology. The main types of users’ emotions and their word association were extracted using the association rules mining technique. The results showed that public engagement with haptic technology was mostly observed in few categories (e.g., gaming). Joy and positive sentiments were strongly associated with the extracted topics. The findings offer new insights into the tendency of utilizing haptic technology across communities. The outcomes can help industries and decision makers understand ways to improve the integration of haptic technology in according to the needs of specific domains.

Risk Factors Influencing Fatal Powered Two-Wheeler At-Fault and Not-at-Fault Crashes: An Application of Spatio-Temporal Hotspot and Association Rule Mining Techniques

Studies have explored the factors influencing the safety of PTWs; however, very little has been carried out to comprehensively investigate the factors influencing fatal PTW crashes while considering the fault status of the rider in crash hotspot areas. This study employs spatio-temporal hotspot analysis and association rule mining techniques to discover hidden associations between crash risk factors that lead to fatal PTW crashes considering the fault status of the rider at statistically significant PTW crash hotspots in South Korea from 2012 to 2017. The results indicate the presence of consecutively fatal PTW crash hotspots concentrated within Korea’s densely populated capital, Seoul, and new hotspots near its periphery. According to the results, violations such as over-speeding and red-light running were critical contributory factors influencing PTW crashes at hotspots during summer and at intersections. Interestingly, while reckless riding was the main traffic violation leading to PTW rider at-fault crashes at hotspots, violations such as improper safety distance and red-light running were strongly associated with PTW rider not-at-fault crashes at hotspots. In addition, while PTW rider at-fault crashes are likely to occur during summer, PTW rider not-at-fault crashes mostly occur during spring. The findings could be used for developing targeted policies for improving PTW safety at hotspots.

Walk or run? An observational study to explore pedestrian crossing decision-making at intersections in Dhaka, Bangladesh applying rules mining technique

To ensure the safety of pedestrians, it is essential to have a comprehensive understanding of their road crossing behaviors, including the factors that influence the decisions they make regarding crossing. One of the crucial crossing behaviors of pedestrians is the crossing pattern, which refers to whether a pedestrian crosses the road by walking or running. Safety of the pedestrians often depends on it as running crossing pattern is one of the riskiest crossing behaviors. However, there is a lack of inclusive studies that explore pedestrians’ decision regarding their crossing pattern. Therefore, this study aimed to identify the significant factors influencing pedestrians' decision regarding their crossing patterns (walk or run) at intersections in Dhaka, Bangladesh, using the chi-square test, and to examine the association between the identified contributory factors and crossing pattern using the association rules mining technique. Pedestrian road crossing behaviors, their characteristics, and traffic characteristics related data were collected from six busy intersections in Dhaka using the videography survey method. Findings of the study showed that walking crossing pattern was strongly associated with the factors such as controlled intersection, narrow road, wide median, female pedestrian, older pedestrian, using two-stage strategy, group crossing, accepting larger gap, using crosswalk, and crossing in front of slower vehicles. Besides, running crossing pattern was strongly associated with uncontrolled intersection, wide road, narrow median, male pedestrian, younger pedestrian, using rolling gap strategy, crossing alone, accepting shorter gap, crossing through conflict zone, and crossing in front of light and faster vehicles. The findings of this study would aid policymakers to develop effective solutions to improve pedestrian safety as well as to design future technologies like automated driving systems.

Data mining of the essential causes of different types of fatal construction accidents

Accident analysis is used to discover the causes of workplace injuries and devise methods for preventing them in the future. There has been little discussion in the previous studies of the specific elements contributing to deadly construction accidents. In contrast to previous studies, this study focuses on the causes of fatal construction accidents based on management factors, unsafe site conditions, and workers' unsafe actions. The association rule mining technique identifies the hidden patterns or knowledge between the root causes of fatal construction accidents, and one hundred meaningful association rules were extracted from the two hundred and fifty-three rules generated. It was discovered that many fatal construction accidents were caused by management factors, unsafe site circumstances, and risky worker behaviors. These analyses can be used to demonstrate plausible cause-and-effect correlations, assisting in building a safer working environment in the construction sector. The study findings can be used more efficiently to design effective inspection procedures and occupational safety initiatives. Finally, the proposed method should be tested in a broader range of construction situations and scenarios to ensure that it is as accurate as possible.

Exploring how feedback through questioning may influence reflective thinking skills based on association rules mining technique

Reflective thinking skills are essential to empower students with 21st century skills. Nevertheless, lack of feedback support has caused students to struggle in developing their reflective thinking skills. Despite the abundance of literature related to feedback practices, the notion of how feedback should be tailored and creatively delivered requires further research. Hence, this study aims to ascertain about how the provision of specific feedback through questioning approach can trigger specific reflective thinking skills using innovative analytical analysis technique, that is, association rules mining. An explorative case study qualitative design was employed and the study was conducted for 14 weeks. Six case-based activities that reflect real-world scenarios were created for students to critically discuss and solve in the blogging environment. Association rules data mining technique was utilised on qualitative data of reflections on both course and individual blogs, to model how feedback through questioning may influence the direction of reflective thinking skills. The findings revealed 16 important association rules and from that only Limited Focal, Open Focal, Brainstorming and Application types of feedback were discovered to lead to important reflective thinking skills development. It was also found that students’ reflective discussion cannot simply move beyond reportive/descriptive thinking and decision-making, despite being pressed by higher level feedback. The overall findings of this study also have implications towards parties like students, instructors, and syllabus and materials designers in terms of how to creatively develop and teach reflective thinking skills via the feedback through questioning approach in educational blogging environments.

A Novel Machine Learning Approach for Android Malware Detection Based on the Co-Existence of Features

This paper proposes a machine learning model based on the co-existence of static features for Android malware detection. The proposed model assumes that Android malware requests an abnormal set of co-existed permissions and APIs in comparing to those requested by benign applications. To prove this assumption, the paper created a new dataset of co-existed permissions and API calls at different levels of combinations, which are the second level, the third level, the fourth level and the fifth level. The extracted datasets of co-existed features at different levels were applied on permissions only, APIs only, permissions and APIs, and APIs and APIs frequencies. To extract the most relevant co-existed features, the frequent pattern growth (FP-growth) algorithm, which is an association rule mining technique, was used. The new datasets were extracted using Android APK samples from the Drebin, Malgenome and MalDroid2020 datasets. To evaluate the proposed model, several conventional machine learning algorithms were used. The results show that the model can successfully classify Android malware with a high accuracy using machine learning algorithms and the co-existence of features. Moreover, the results show that the achieved classification accuracy depends on the classifier and the type of co-existed features. The maximum accuracy, which is 98%, was achieved using the Random Forest algorithm and the co-existence of permissions features at the second combination level. Furthermore, the results show that the proposed approach outperforms the state-of-the-art model. Using Malgenome dataset, the proposed approach achieved an accuracy of about 98%, while the state-of-the-art achieved an accuracy of about 87%. In addition, the experiments show that using the Drebin dataset, the proposed approach achieved an accuracy of about 95%, while the state-of-the-art achieved an accuracy of about 93%.

Exploring the Tradeoff between Utility and Privacy for Analyzing Stock Trend

In this study of exploring the tradeoff between utility and privacy, we have used the S &amp; P 500 data as a proof of concept, for Association Rule Data Mining using privacy-preserved data. The purpose of choosing S &amp; P 500 was that unlike synthetically generated data, S &amp; P 500 index though public, was real-world data nonetheless and hence incorporated in it all the political and social events that occurred over the period of time under consideration. Secondly, we could use stock price as a quasi-identifier. Any seasoned stockbroker would be able to identify the organization simply by looking at the stock price fluctuations over a period even if the ticker information is removed. And stock price is the most important variable used in any data mining related with stock market. Hence, we had to perturb this one variable in order to test for anonymity and utility. Finally, the data being numerical in nature helped us utilize the statistical data perturbation techniques. Association rule mining techniques were first applied to the original data. The data was then perturbed and, once again, using binary similarity algorithms, trend matching was performed on the identified stocks from the original data. The three tests of different random data generations were evaluated for data utility and privacy. The results re-emphasized on the tradeoff between the data utility and privacy where greater perturbation meant greater privacy but higher utility loss and vice versa.

Revolutionizing supermarket services with hierarchical association rule mining

The use of association rule mining techniques has become a focal point for many researchers seeking a better understanding of consumer behavior. By analyzing the relationships between products and their placement in aisles, valuable insights can be gained into the factors that influence product preservation in large-scale distribution environments. This approach has the potential to inform better decision-making processes and optimize product preservation outcomes, despite some limitations in the quality of the data available. Additionally, a hybridization approach was adopted by incorporating transactions from clients participating in a loyalty program to encourage large-scale distributions to gain a better understanding of customer behavior and improve their purchasing strategies. The goal of this research is to promote consistency between the real-world and virtual representations of customer behavior, ultimately leading to improved purchasing outcomes for large-scale distributions.

The Hash base Apriori Technique for Association Rule Mining and Data Sanitization

Under different circumstances, private information is exposed, and it must be sanitised before even being shared to address privacy issues. Data mining techniques can collect large amounts of data in a short amount of time. The information gathered by the powerful machine learning techniques may identify the most sensitive content, which pertains to an individual or organization. The degree of sensitivity of data belonging to a business or an agency might vary. Only approved individuals and organizations have access to this information. As a result, using access limitations to confirm the security of complicated data is not a complete operation. It can have an impact on the utility of a data mining solution, and the user may be able to re-identify sensitive data. To introduce instruments to find a mechanism for the security of confidential information. Finding ways to secure confidential data by developing data mining tools and procedures that can be applied to databases, despite the fact that this diminishes the data mining results' trust worthiness. We proposed a data sanitization strategy in this article that uses a frequent itemset classification approach with a modified apriori algorithm. The problem is to maintain intelligence information for vital arrangements while preventing the numerous exposures of company rule mining at the same time. Data sanitization strategy is used to perform a thorough investigation of numerous sequential pattern algorithms for ensuring the privacy of large amounts of data. Our research shows that our approach is efficient, scalable, and provides meaningful correction when compared to other methods used in existing systems.&#x0D; Keywords: Association Rule Mining, Classification, Frequent itemset, data privacy, data hiding, rule generation, support, confidence and transactional dataset