scholarly journals Association rule mining approach: evaluating pre-purchase risk intentions in the online second-hand goods market

2019 ◽  
Vol 10 (4) ◽  
pp. 669-688
Author(s):  
Abdul Bashiru Jibril ◽  
Michael Adu Kwarteng ◽  
Christina Appiah-Nimo ◽  
Michal Pilik
Keyword(s):  
Risk Factors ◽  
Czech Republic ◽  
Association Rule ◽  
Association Rule Mining ◽  
Value Added ◽  
Data Mining Algorithm ◽  
Rule Mining ◽  
The Czech Republic ◽  
Online Transactions ◽  
Buying Behaviour

Research background: A considerable amount of research has been conducted on the riskiness associated with online transactions in general. However, few studies have paid particular attention to the risk of online second-hand goods transactions. We, therefore, argue in this paper that, while online transactions pose several risks to consumers, the addition of second-hand goods intensifies the risks to the user. As the risk factors brought about by the online second-hand goods transactions persist, the magnitude of such risk inherent in the customer in question has not clearly emerged. Purpose of the article: This paper aims at eliciting the magnitude of risky components aligned with the tendency to connect online in search of second-hand goods. Again, providing insight into demographic variables in relation to the pre-purchasing risk factors; averting customers to connect online in search of second-hand goods stands as one of the key reasons for the present study. Methods: The research adopts a data mining algorithm, notably the Association rule mining to glean relevant patterns in the data accrued from the Czech Republic, premised on risk components governing the online buying behaviour of second-hand goods. To this end, a simple random technique was adopted to gauge the views of e-shoppers in the Czech Republic on online second-hand goods transactions; with 329 out of 411 respondents eligible for our analysis. Findings & Value added: The results of the association rule technique have revealed that respondents within the gender frame are both adamant to hook-up online, in spite of the fact that they have shopped online, yet do not think of looking at second-hand goods sites because of some risky influence inherent in them, even if the respondent is an ordinary personal-user of online transactions. In all these developments, the research concludes that the second-hand industry needs to redesign the websites with much attention to reinforce stringent measures that will give better assurance of the risk factors, which will tend to avert the customer from connecting via the Internet in pursuit of second-hand goods.

Research on Association Rule Mining Algorithm Based on Distributed Data

2014 ◽  
Vol 998-999 ◽  
pp. 899-902 ◽  
Author(s):  
Cheng Luo ◽  
Ying Chen
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Large Scale ◽  
Frequent Itemsets ◽  
Network Communication ◽  
Data Mining Algorithm ◽  
Distributed Data ◽  
Rule Mining ◽  
Mining Algorithm

Existing data miming algorithms have mostly implemented data mining under centralized environment, but the large-scale database exists in the distributed form. According to the existing problem of the distributed data mining algorithm FDM and its improved algorithms, which exist the problem that the frequent itemsets are lost and network communication cost too much. This paper proposes a association rule mining algorithm based on distributed data (ARADD). The mapping marks the array mechanism is included in the ARADD algorithm, which can not only keep the integrity of the frequent itemsets, but also reduces the cost of network communication. The efficiency of algorithm is proved in the experiment.

scholarly journals Human Resource Allocation Based on Fuzzy Data Mining Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
You Wu ◽  
Zheng Wang ◽  
Shengqi Wang
Keyword(s):  
Data Mining ◽  
Human Resource ◽  
Business Process ◽  
Association Rule ◽  
Association Rule Mining ◽  
Large Scale ◽  
Data Mining Algorithm ◽  
Rule Mining ◽  
Mining Algorithm ◽  
Database Technology

Data mining is currently a frontier research topic in the field of information and database technology. It is recognized as one of the most promising key technologies. Data mining involves multiple technologies, such as mathematical statistics, fuzzy theory, neural networks, and artificial intelligence, with relatively high technical content. The realization is also difficult. In this article, we have studied the basic concepts, processes, and algorithms of association rule mining technology. Aiming at large-scale database applications, in order to improve the efficiency of data mining, we proposed an incremental association rule mining algorithm based on clustering, that is, using fast clustering. First, the feasibility of realizing performance appraisal data mining is studied; then, the business process needed to realize the information system is analyzed, the business process-related links and the corresponding data input interface are designed, and then the data process to realize the data processing is designed, including data foundation and database model. Aiming at the high efficiency of large-scale database mining, database development tools are used to implement the specific system settings and program design of this algorithm. Incorporated into the human resource management system of colleges and universities, they carried out successful association broadcasting, realized visualization, and finally discovered valuable information.

scholarly journals Another Look at Obesity Paradox in Acute Ischemic Stroke: Association Rule Mining

2021 ◽  
Vol 12 (1) ◽  
pp. 16
Author(s):  
Pum-Jun Kim ◽  
Chulho Kim ◽  
Sang-Hwa Lee ◽  
Jong-Hee Shon ◽  
Youngsuk Kwon ◽  
...  
Keyword(s):  
Risk Factors ◽  
Ischemic Stroke ◽  
Beneficial Effect ◽  
Acute Ischemic Stroke ◽  
Association Rule ◽  
Association Rule Mining ◽  
Obesity Paradox ◽  
Stroke Severity ◽  
Obese Patients ◽  
Rule Mining

Though obesity is generally associated with the development of cardiovascular disease (CVD) risk factors, previous reports have also reported that obesity has a beneficial effect on CVD outcomes. We aimed to verify the existing obesity paradox through binary logistic regression (BLR) and clarify the paradox via association rule mining (ARM). Patients with acute ischemic stroke (AIS) were assessed for their 3-month functional outcome using the modified Rankin Scale (mRS) score. Predictors for poor outcome (mRS 3–6) were analyzed through BLR, and ARM was performed to find out which combination of risk factors was concurrently associated with good outcomes using maximal support, confidence, and lift values. Among 2580 patients with AIS, being obese (OR [odds ratio], 0.78; 95% CI, 0.62–0.99) had beneficial effects on the outcome at 3 months in BLR analysis. In addition, the ARM algorithm showed obese patients with good outcomes were also associated with an age less than 55 years and mild stroke severity. While BLR analysis showed a beneficial effect of obesity on stroke outcome, in ARM analysis, obese patients had a relatively good combination of risk factor profiles compared to normal BMI patients. These results may partially explain the obesity paradox phenomenon in AIS patients.

Using association rule mining to identify risk factors for early childhood caries

2015 ◽  
Vol 122 (2) ◽  
pp. 175-181 ◽  
Author(s):  
Vladimir Ivančević ◽  
Ivan Tušek ◽  
Jasmina Tušek ◽  
Marko Knežević ◽  
Salaheddin Elheshk ◽  
...  
Keyword(s):  
Risk Factors ◽  
Early Childhood ◽  
Association Rule ◽  
Association Rule Mining ◽  
Early Childhood Caries ◽  
Rule Mining ◽  
Childhood Caries

scholarly journals Design of Multi-level Teaching System Based on Association Rule Mining

2016 ◽  
Vol 11 (11) ◽  
pp. 43 ◽  
Author(s):  
Hong-yan He ◽  
Hui-ping, Zhang ◽  
Hong-fang Luo
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Learning Ability ◽  
Data Mining Algorithm ◽  
Rule Mining ◽  
Teaching System ◽  
System A ◽  
Overall Performance ◽  
Multi Level ◽  
Different Parts

In order to improve the overall performance for the multi-level teaching system, a system with Multi-strata teaching is designed. It divides the whole class into smaller parts based on their knowledge level and learning ability and teach students in accordance with their aptitude. The system used the model of C/S, and applies ASP in the interactive user interface. The data mining algorithm is also presented in the study. The system was tested with practical data. The results show that with the teaching system teachers can separate students into different parts and get a very good idea about how much students can learn.

scholarly journals Detecting Lifestyle Risk Factors for Chronic Kidney Disease With Comorbidities: Association Rule Mining Analysis of Web-Based Survey Data

10.2196/14204 ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. e14204 ◽  
Author(s):  
Suyuan Peng ◽  
Feichen Shen ◽  
Andrew Wen ◽  
Liwei Wang ◽  
Yadan Fan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Association Rule ◽  
Chronic Conditions ◽  
Association Rule Mining ◽  
Lifestyle Modification ◽  
Rule Mining ◽  
Web Based ◽  
Lifestyle Risk Factors ◽  
Lifestyle Choices ◽  
Lifestyle Risk

Background The rise in the number of patients with chronic kidney disease (CKD) and consequent end-stage renal disease necessitating renal replacement therapy has placed a significant strain on health care. The rate of progression of CKD is influenced by both modifiable and unmodifiable risk factors. Identification of modifiable risk factors, such as lifestyle choices, is vital in informing strategies toward renoprotection. Modification of unhealthy lifestyle choices lessens the risk of CKD progression and associated comorbidities, although the lifestyle risk factors and modification strategies may vary with different comorbidities (eg, diabetes, hypertension). However, there are limited studies on suitable lifestyle interventions for CKD patients with comorbidities. Objective The objectives of our study are to (1) identify the lifestyle risk factors for CKD with common comorbid chronic conditions using a US nationwide survey in combination with literature mining, and (2) demonstrate the potential effectiveness of association rule mining (ARM) analysis for the aforementioned task, which can be generalized for similar tasks associated with noncommunicable diseases (NCDs). Methods We applied ARM to identify lifestyle risk factors for CKD progression with comorbidities (cardiovascular disease, chronic pulmonary disease, rheumatoid arthritis, diabetes, and cancer) using questionnaire data for 450,000 participants collected from the Behavioral Risk Factor Surveillance System (BRFSS) 2017. The BRFSS is a Web-based resource, which includes demographic information, chronic health conditions, fruit and vegetable consumption, and sugar- or salt-related behavior. To enrich the BRFSS questionnaire, the Semantic MEDLINE Database was also mined to identify lifestyle risk factors. Results The results suggest that lifestyle modification for CKD varies among different comorbidities. For example, the lifestyle modification of CKD with cardiovascular disease needs to focus on increasing aerobic capacity by improving muscle strength or functional ability. For CKD patients with chronic pulmonary disease or rheumatoid arthritis, lifestyle modification should be high dietary fiber intake and participation in moderate-intensity exercise. Meanwhile, the management of CKD patients with diabetes focuses on exercise and weight loss predominantly. Conclusions We have demonstrated the use of ARM to identify lifestyle risk factors for CKD with common comorbid chronic conditions using data from BRFSS 2017. Our methods can be generalized to advance chronic disease management with more focused and optimized lifestyle modification of NCDs.

scholarly journals Coronary Heart Event Analysis with Association Rule Mining

2013 ◽  
Vol 1 (1) ◽  
pp. 44-48
Author(s):  
B. Gomathy ◽  
S.M. Ramesh ◽  
A. Shanmugam
Keyword(s):  
Risk Factors ◽  
Data Mining ◽  
Blood Pressure ◽  
Family History ◽  
Association Rule ◽  
Association Rule Mining ◽  
Bypass Graft ◽  
Density Lipoprotein ◽  
Rule Mining ◽  
History Of

Coronary heart disease (CHD) is one of the major causes of disability in adults as well as one of the main causes of death in the developed countries. Although significant progress has been made in the diagnosis and treatment of CHD, further investigation is still needed. The objective of this study was to develop the assessment of heart event-risk factors targeting in the reduction of CHD events using Association Rule Mining. The risk factors investigated were: 1) before the event: a) non modifiable—age, sex, and family history for premature CHD, b) modifiable—smoking before the event, history of hypertension, and history of diabetes; and 2) after the event: modifiable—smoking after the event, systolic blood pressure, diastolic blood pressure, total cholesterol, high density lipoprotein, low-density lipoprotein, triglycerides, and glucose. The events investigated were: myocardial infarction (MI), percutaneous coronary intervention (PCI), and coronary artery bypass graft surgery (CABG).Data-mining analysis was carried out using the Association Rule Mining for the afore mentioned three events using five different splitting criteria for larger datasets. The most important risk factors, as extracted from the classification rules analysis were: 1) for MI, age, smoking, and history of hypertension; 2) for PCI, family history, history of hypertension, and history of diabetes; and 3) for CABG, age, history of hypertension, and smoking. It is anticipated that data mining could help in the identification of high and low risk subgroups of subjects, a decisive factor for the selection of therapy, i.e., medical or surgical.

scholarly journals Detecting Lifestyle Risk Factors for Chronic Kidney Disease With Comorbidities: Association Rule Mining Analysis of Web-Based Survey Data (Preprint)

2019 ◽  
Author(s):  
Suyuan Peng ◽  
Feichen Shen ◽  
Andrew Wen ◽  
Liwei Wang ◽  
Yadan Fan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Association Rule ◽  
Chronic Conditions ◽  
Association Rule Mining ◽  
Lifestyle Modification ◽  
Rule Mining ◽  
Web Based ◽  
Lifestyle Risk Factors ◽  
Lifestyle Choices ◽  
Lifestyle Risk

BACKGROUND The rise in the number of patients with chronic kidney disease (CKD) and consequent end-stage renal disease necessitating renal replacement therapy has placed a significant strain on health care. The rate of progression of CKD is influenced by both modifiable and unmodifiable risk factors. Identification of modifiable risk factors, such as lifestyle choices, is vital in informing strategies toward renoprotection. Modification of unhealthy lifestyle choices lessens the risk of CKD progression and associated comorbidities, although the lifestyle risk factors and modification strategies may vary with different comorbidities (eg, diabetes, hypertension). However, there are limited studies on suitable lifestyle interventions for CKD patients with comorbidities. OBJECTIVE The objectives of our study are to (1) identify the lifestyle risk factors for CKD with common comorbid chronic conditions using a US nationwide survey in combination with literature mining, and (2) demonstrate the potential effectiveness of association rule mining (ARM) analysis for the aforementioned task, which can be generalized for similar tasks associated with noncommunicable diseases (NCDs). METHODS We applied ARM to identify lifestyle risk factors for CKD progression with comorbidities (cardiovascular disease, chronic pulmonary disease, rheumatoid arthritis, diabetes, and cancer) using questionnaire data for 450,000 participants collected from the Behavioral Risk Factor Surveillance System (BRFSS) 2017. The BRFSS is a Web-based resource, which includes demographic information, chronic health conditions, fruit and vegetable consumption, and sugar- or salt-related behavior. To enrich the BRFSS questionnaire, the Semantic MEDLINE Database was also mined to identify lifestyle risk factors. RESULTS The results suggest that lifestyle modification for CKD varies among different comorbidities. For example, the lifestyle modification of CKD with cardiovascular disease needs to focus on increasing aerobic capacity by improving muscle strength or functional ability. For CKD patients with chronic pulmonary disease or rheumatoid arthritis, lifestyle modification should be high dietary fiber intake and participation in moderate-intensity exercise. Meanwhile, the management of CKD patients with diabetes focuses on exercise and weight loss predominantly. CONCLUSIONS We have demonstrated the use of ARM to identify lifestyle risk factors for CKD with common comorbid chronic conditions using data from BRFSS 2017. Our methods can be generalized to advance chronic disease management with more focused and optimized lifestyle modification of NCDs.

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