Association Rule Mining Method Research Articles

Outburst prediction and influencing factors analysis based on Boruta-Apriori and BO-SVM algorithms

The influencing factors of coal and gas outburst are complex, and now the accuracy and efficiency of outburst prediction are not high. In order to obtain the effective features from influencing factors and realize the accurate and fast dynamic prediction of coal and gas outburst, this article proposes an outburst prediction model based on the coupling of feature selection and intelligent optimization classifier. Firstly, in view of the redundancy and irrelevance of the influencing factors of coal and gas outburst, we use Boruta feature selection method to obtain the optimal feature subset from influencing factors of coal and gas outburst. Secondly, based on Apriori association rules mining method, the internal association relationship between coal and gas outburst influencing factors is mined, and the strong association rules existing in the influencing factors and samples that affect the classification of coal and gas outburst are extracted. Finally, svm is used to classify coal and gas outburst based on the above obtained optimal feature subset and sample data, and Bayesian optimization algorithm is used to optimize the kernel parameters of svm, and the coal and gas outburst pattern recognition prediction model is established, which is compared with the existing coal and gas outburst prediction model in literatures. Compared with the method of feature selection and association rules mining alone, the proposed model achieves the highest prediction accuracy of 93% when the feature dimension is 3, which is higher than that of Apriori association rules and Boruta feature selection, and the classification accuracy is significantly improved. However, the feature dimension decreased significantly, the results show that the proposed model is better than other prediction models, which further verifies the accuracy and applicability of the coupling prediction model.

Discovery of new safety knowledge from mining large injury dataset in construction

Safety in construction continues to remain as the 3rd most serious occupational problem worldwide. Previous research mainly used questionnaire surveys or interviews to obtain “cross-sectional” data, which may be difficult to extract patterns and gain new knowledge for improving safety performance. This research aims to discover the characteristics, patterns, mechanisms and knowledge from mining a large injury dataset, and to develop strategies for mitigating safety risks, and reducing accidents and injuries so that to improve construction safety performance. A large set of injury and incident data recorded by construction companies over a period of 11 years was used in this research. Statistical analysis, visualisation analysis and association rule mining (ARM) methods were applied to mine and analyse this large dataset. “Injury time”, “injured body locations, organs and systems”, “age distribution of injured”, “causes, nature and relationships of injury, and injured body parts” “changing trends of incidents”, were analysed in depth to discover new safety knowledge. Based on the knowledge and mechanism discovered, this study proposed five strategies for improving construction safety performance covering safety skills, safety awareness, safety protection and emergency response. This research would be valuable to both researchers and practitioners. Researchers would benefit from gaining a deeper understanding of the characteristics of construction safety injuries, application of data mining methods, and relevant future research directions, while practitioners would benefit by learning about the strategies proposed to mitigate injury risks and prevent incidents.

Patterns of achievement attribution of Chinese adults and their sociodemographic characteristics and psychological outcomes: A large-sample longitudinal study

The majority of research on achievement attributions cited limitations in sample sizes, convenient sampling, self-rated questionnaires, cross-sectional designs, variable-centered approaches, and lack of attention to cultural diversity. To address these issues, the current study conducted a large-sample longitudinal study on achievement attributions along person-centered approaches in China. Study 1 performed a latent profile analysis (LPA) on 28,768 participants from the China Family Panel Studies (CFPS), and identified four focal patterns of achievement attribution among Chinese adults (i.e., effort–determinism, effort–betterment, inherited, and multi-determined). Study 2 discovered the sociodemographic characteristics of groups with different patterns of achievement attribution using the method of association rules mining. Study 3 tested the effects of patterns of achievement attribution on several psychological outcomes (i.e., well-being, health, interpersonal life, and social attitudes) based on the 2010, 2012, 2014, and 2016 waves of CFPS involving indicators rated by respondents and professionally trained interviewers. The results demonstrated that people with the inherited pattern were more negative in terms of well-being, interpersonal life, and social attitude, whereas those with the effort–determinism and effort–betterment patterns exhibited better psychological adaptation, a promising and optimistic view of life, and more positive mental states.

Patterns of near-crash events in a naturalistic driving dataset: Applying rules mining

This study aims to explore the associations between near-crash events and road geometry and trip features by investigating a naturalistic driving dataset and a corresponding roadway inventory dataset using an association rule mining method – the Apriori algorithm. To provide more insights into near-crash behavior, this study classified near-crash events into two severity levels: trivial near-crash events (-7.5 g ≤ deceleration rate ≤ -4.5 g) and non-trivial near-crash events (≤-7.5 g). From the perspective of descriptive statistics, the frequency of the itemsets, a set of categories of various variables, generated by the Apriori algorithm suggests that near-crash events are highly associated with several factors, including roadways without access control, driving during non-peak hours, roadways without a shoulder or a median, roadways with the minor arterial functional class, and roadways with a speed limit between 30 and 60 mph. By comparing the frequency of the occurrence of the itemset during trivial and non-trivial near-crash events, the results indicate that the length of the trip is a strong indicator of the near-crash event type. The results show that non-trivial near-crash events are more likely to occur if the trip is longer than 2 h. After applying the association rule mining algorithm, more interesting patterns for the two near-crash events were generated through the rules. The main findings include: 1) trivial near-crash events are more likely to occur on roadways without a median and shoulder that have a relatively lower functional class; 2) relatively higher functional roadways with relatively wide medians and shoulders could be an intriguing combination for non-trivial near-crash events; 3) non-trivial near-crash events often occur on long trips (more than 2 h); 4) congestion on roadways that have a lower functional class is a dominant rule associating with the high frequency of non-trivial near-crash events. This study associates near-crash events and the corresponding road geometry and trip features to provide a unique understanding of near-crash events.

Data-driven decision support system for managing item allocation in an ASRS: A framework development and a case study

When dealing with Automated Storage and Retrieval Systems (ASRS), the allocation of items to the most convenient storage location depends on the vast amount of data produced internally (e.g., Enterprise Resource Planning, Manufacturing Enterprise Systems) and externally (e.g. Supply Chain Management). Moreover, a proper item allocation in the warehouse has a strong influence on the warehouse saturation levels and picking times.In this perspective, the present work proposes the application of data-driven algorithms for managing items in an Automated Storage and Retrieval System (ASRS) in order to reduce the picking times and storage space.Specifically, a four-layer framework is adopted for collecting data produced by different information sources and analyzing them through a data-driven approach. The analytics layer is performed by combining the Association Rule Mining (ARM) technique, to investigate the network of influences among data collected, and a simulation approach for assessing the feasibility of the proposed implementation. The Association Rule Mining allows company managers to identify the components that should be located on the same tray in the ASRS, defining the couples of items frequently picked together in order to reduce the total picking time. The proposed approach is applied to the case study of a shoe manufacturing company to explain the research approach and show how the implementation of the data-driven methodology can provide valuable support in defining item allocation and picking rules. The proposed Association Rule Mining method is new in this context and it has shown a positive impact in comparison to traditional solutions of warehouse management, providing a complete overview of the items’ interactions and identifying communities of items that define local and global patterns and locate influential entities.

A Systematic Assessment of Numerical Association Rule Mining Methods

A Systematic Assessment of Numerical Association Rule Mining Methods

A Novel Association Rule Mining Method for Streaming Temporal Data

A Novel Association Rule Mining Method for Streaming Temporal Data

Core and Spectrum Allocation Based on Association Rules Mining in Spectrally and Spatially Elastic Optical Networks

The combination of space division multiplexing technology with elastic optical networks allows to overcome the possible capacity crunch in backbone networks and also improves network flexibility by jointly managing spectral and spatial resources. However, against this background implemented by multi-core fibers, the interaction between spatial modes will appear as signal crosstalk, thereby affecting the service’s transmission quality. Spectrum resources without crosstalk are always preferred for the services to guarantee quality of service, possibly resulting in the spectrum fragmentation. Conversely, if resources with crosstalk are selected for services to reduce fragments, it may lead to serious crosstalk on the services already carried in the adjacent cores. To achieve a tradeoff between these two factors, this paper firstly exploits the association rule mining method to quantitatively analyze the potential correlation between them. By executing FP-growth mining algorithm, rules not beneficial to service provisioning will be filtered out. Then, an association rules-based core and spectrum assignment algorithm is presented, considering transmission requirements for different levels of services. Simulation results indicate the presented strategy can decrease the proportion of services affected by crosstalk and also reduce the possibility of fragments generation. Additionally, it can effectively make improvement on the blocking and resource utilization.

MANIEA: a microbial association network inference method based on improved Eclat association rule mining algorithm.

Modeling microbiome systems as complex networks are known as the problem of network inference. Microbial association network inference is of great significance in applications on clinical diagnosis, disease treatment, pathological analysis, etc. However, most current network inference methods focus on mining strong pairwise associations between microorganisms, which is defective in reflecting the comprehensive interactive patterns participated by multiple microorganisms. It is also possible that the microorganisms involved in the generated network are not dominant in the microbiome due to the mere focus on the strength of pairwise associations. Some scholars tried to mine comprehensive microbial associations by association rule mining methods, but the adopted algorithms are relatively basic and have severe limitations such as low calculation efficiency, lacking the ability of mining negative correlations and high redundancy in results, making it difficult to mine high-quality microbial association rules and accurately infer microbial association networks. We proposed a microbial association network inference method 'MANIEA' based on the improved Eclat algorithm for mining positive and negative microbial association rules. We also proposed a new method for transforming association rules into microbial association networks, which can effectively demonstrate the co-occurrence and causal correlations in association rules. An experiment was conducted on three authentic microbial abundance datasets to compare the 'MANIEA' with currently popular network inference methods, which demonstrated that the proposed 'MANIEA' show advantages in aspects of correlation forms, computation efficiency, adjustability and network characteristics. The algorithms and data are available at: https://github.com/MaidiL/MANIEA.

Association rule mining method for the identification of internet use

Internet penetration in the majority of Indonesian cities has exponentially increased, as seen from the increasing number of internet users in schools, businesses and society in general. The purpose of internet use varies to include searching for information, sending emails, chatting, entertainment, as well as buying and selling goods/services, among other reasons. However, it is compelling to note that one of the factors affecting internet use is gender. Hence, this research aims to reveal the different patterns in internet use by considering gender differences. The data comes from the National Social Economic Survey conducted in the East Java Province, and association rule is used for the data mining method. The results from examining one itemset show that both male and female genders mostly use the internet to access social media. However, the outcomes are different for other itemsets. The results from analysing two itemsets based on male and female groups reveal that both genders use the internet for financial services and news. The use of association rule mining to examine three itemsets demonstrates that most male users simultaneously access emails, financial services and news. Meanwhile, most female users access financial services, goods/services and news.

Investigating Health Equity and Healthcare Needs among Immigrant Women Using the Association Rule Mining Method.

Equitable access to healthcare services is a major concern among immigrant women. Thus, this study investigated the relationship between socioeconomic characteristics and healthcare needs among immigrant women in Taiwan. The secondary data was obtained from “Survey of Foreign and Chinese Spouses’ Living Requirements, 2008”, which was administered to 5848 immigrant women by the Ministry of the Interior, Taiwan. Additionally, descriptive statistics and significance tests were used to analyze the data, after which the association rule mining algorithm was applied to determine the relationship between socioeconomic characteristics and healthcare needs. According to the findings, the top three healthcare needs were providing medical allowances (52.53%), child health checkups (16.74%), and parental knowledge and pre- and post-natal guidance (8.31%). Based on the association analysis, the main barrier to the women’s healthcare needs was “financial pressure”. This study also found that nationality, socioeconomic status, and duration of residence were associated with such needs, while health inequality among aged immigrant women was due to economic and physical factors. Finally, the association analysis found that the women’s healthcare problems included economic, socio-cultural, and gender weakness, while “economic inequality” and “women’s health” were interrelated.

Network Teaching Technology Based on Big Data Mining and Information Fusion

With the continuous development of modern multimedia technology, the integration of computer technology into the teaching of various subjects has become a trend of the times. The application of computer media and network technology in mathematics teaching improves the integration of mathematics teaching and the integration of resources. A mathematics teaching network media fusion technology is proposed based on big data mining and information fusion, which combines the characteristics of multimedia and network technology in opening, creativity, subjectivity, and so on, and the database model of mathematics teaching is constructed. The multithread integrated scheduling method is used to design the mathematics teaching database model, the fuzzy control method is used to control the multimedia in mathematics teaching, and the big data association rule mining method is used to realize the information fusion of mathematics teaching resources. The optimization and integration of mathematics teaching resources and adaptive scheduling are realized under the technology of computer media and network, and the level of mathematics teaching is improved. The test results show that using this method to design the computer network media of mathematics teaching has a better ability of integrating and dispatching mathematics teaching resources, and the integration of mathematics teaching resources is stronger, which promotes the improvement of mathematics teaching level.

A novel association rule mining method for the identification of rare functional dependencies in Complex Technical Infrastructures from alarm data

This work presents a data-driven method for identifying rare functional dependencies among components of different systems of Complex Technical Infrastructures (CTIs) from large-scale databases of alarm messages. It is based on the representation of the alarm data in a binary form, the use of a novel association rule mining algorithm properly tailored for discovering rare dependencies among components of different systems and on the identification of groups of functionally dependent components. The proposed method is applied to a synthetic alarm database generated by a simulated CTI model and to a real large-scale database of alarms collected in the CTI of CERN (European Organization for Nuclear Research). The obtained results show the effectiveness of the proposed method.

Impact factor based data sanitization in association rule mining

Abstract Data sanitization process is employed to market the sharing of transactional databases among organizations and businesses, and alleviates concerns for people and organizations regarding the disclosure of sensitive patterns sanitization process converts the source database into a released database so that unauthorized person cannot identify the sensitive patterns and so data confidentiality is maintained using association rule mining method. This process strongly relies on the minimizing the impact of knowledge sanitization on the info utility by minimizing the amount of lost patterns within the sort of non– sensitive patterns which are not mined from sanitized database. This study proposes a knowledge sanitization algorithm to cover sensitive patterns within the sort of frequent item sets from the database while controlling the impact of sanitization on the data utility using estimation of impact factor of every modification on non-sensitive item sets. In some applications like market basket analysis, Association Rule Mining (ARM) has recently gained more attention in businesses where the regularities within the customer purchasing behavior are found. On the other hand, these discovered patterns may pose a threat to the privacy of data holder; therefore, these patterns should be hidden before data sharing in such a way that the adversaries cannot discover the regularities in customer purchasing behavior.

Potentially modifiable risk factors for 30-day unplanned hospital readmission preventive intervention-A data mining and statistical analysis.

Unplanned hospital readmissions have a high prevalence and substantial healthcare costs. Preventive intervention during hospitalization holds the potential for reducing readmission risk. However, it is challenging to develop individualized interventions during hospitalization because the causes of readmissions have not been clearly known and because patients are heterogeneous. This work aimed to identify potentially modifiable risk factors of readmission to help clinicians better plan and prioritize interventions for different patient subgroups during hospitalization. We performed the analysis of associations between the changes of potentially modifiable risk factors and the change of readmission status with association rule mining and statistical methods. Twenty-nine risk factors were identified from the association rules, and twenty-five of them were potentially modifiable. The association rules with potentially modifiable risk factors can be recommended to different patient subgroups to support the development of customized readmission preventive interventions.

Research and Application of a Multidimensional Association Rules Mining Method Based on OLAP

As to the problems of low data mining efficiency, less dimensionality, and low accuracy of traditional multidimensional association rules in the university big data environment, an OLAP-based multi-dimensional association rule mining method is proposed, which combines hash function and marked transaction compression technology to solve the problem of excessive or redundant candidate sets in the Apriori algorithm, and uses On Line Analytical Processing to manage the intermediate data in the association mining process , in order to reduce the time overhead caused by repeated calculations. To verify the validity of the proposed method, a learning situation analysis system is constructed in the field of colleges and universities. The multi-dimensional association rules mining method is used to analyze more than 21,000 desensitized real data, in order to mine the key factors affecting students' academic performance. The experimental results show that the proposed multi-dimensional mining model has good mining results and significantly improves the time performance.

Integrating rough set theory with customer satisfaction to construct a novel approach for mining product design rules

When users choose a product, they consider the emotional experience triggered by the product form. In view of the fact that traditional kansei engineering can not effectively reflect the complex and changeable psychological factors of users, and it has not explored the complex relationship between customer satisfaction and perceptual demand characteristics. To address this problem, some uncertainty techniques including rough sets and fuzzy sets are applied to capture more accurate emotion knowledge. Therefore, this research proposes an integrated evaluation gird method (EGM), rough set theory (RST), continuous fuzzy kano model (CFKM), fuzzy weighted association rule mining method to extract the significant relationship between user needs and product morphological features. The EGM is applied to analyze the attractive factor of morphological characteristics of the product, and then the demand items with the highest satisfaction are analyzed through CFKM. The semantic difference method is combined to construct a decision table, and through attribute reduction and importance calculation to obtain the weight of the core product design items. In order to explore the non-linear relationship between design elements and kansei images, the fuzzy weighted association rule mining method was applied to obtain the set of frequent fuzzy weighted association rules based on evidence theory’s reliability indices of minimum support and confidence so as to realize user demand-driven product design. Taking the design of electric bicycle as an example, the experiment results show that the proposed method can help companies or designers develop products to generate good solutions for customer need.

Cyber intrusion detection through association rule mining on multi-source logs

Security logs in cloud environment like intrusion detection system (IDS) logs, firewall logs, and system logs provide historical information describing potential security risks. However, the use of logs for cyber intrusion detection relies heavily on expert knowledge. It is very difficult for the non-expert to identify these intrusion behaviors. This paper proposes a new method for mining association rules from multi-source logs to detect various intrusion behaviors in the cloud computing platform. In this method, a rule base is constructed to detect cyber intrusion. An adaptive approach is used to speed up the calculation of the association rule mining, in which the decision depends on the time complexity of the algorithm. Various cyber-attacks are simulated in the verification experiments which show the calculation speed of the proposed method is faster than other algorithms. Furthermore, compared with other methods, the performance of the proposed intrusion detection method is better than others in term of precision, recall, and f-measure.

A Fuzzy Association Rules Mining Analysis of the Influencing Factors on the Failure of oBike in Taiwan

The sharing economy has become an important issue in recent years. Many researchers have paid attention to its application around the world. The sharing of bikes, as one of the major applications of the sharing economy, has shown its advantage in the realm of environmental protection and low energy consumption. However, bike-sharing system has encountered problems in certain regions. This arouses the concern about the sustainable development of the bike-sharing system. This research focused on the failure case of oBike in Taiwan. This research used text mining and fuzzy association rules mining methods to evaluate Taiwan’s public opinion about the oBike in order to verify the reasons for oBike’s failure in Taiwan. This study also made a comparison between the bike-sharing system in Mainland China and Taiwan. The research results explored the factors of oBike’s failure in Taiwan and showcased the problems of bike-sharing systems in different regions. The research results also offer useful information for bike-sharing companies and the authorities concerned in order to develop a sustainable bike-sharing system.

Association rules mining between service demands and remanufacturing services

AbstractThe potential relationship between service demands and remanufacturing services (RMS) is essential to make the decision of a RMS plan accurately and improve the efficiency and benefit. In the traditional association rule mining methods, a large number of candidate sets affect the mining efficiency, and the results are not easy for customers to understand. Therefore, a mining method based on binary particle swarm optimization ant colony algorithm to discover service demands and remanufacture services association rules is proposed. This method preprocesses the RMS records, converts them into a binary matrix, and uses the improved ant colony algorithm to mine the maximum frequent itemset. Because the particle swarm algorithm determines the initial pheromone concentration of the ant colony, it avoids the blindness of the ant colony, effectively enhances the searchability of the algorithm, and makes association rule mining faster and more accurate. Finally, a set of historical RMS record data of straightening machine is used to test the validity and feasibility of this method by extracting valid association rules to guide the design of RMS scheme for straightening machine parts.