Association Rule Mining Technology Research Articles

Automatic Integration Algorithm of Vocal Performance Learning Materials Based on Multidimensional Association Rules

As computer and multimedia technology advances and as the variety of knowledge display forms expands, there are more ways for people to obtain knowledge and information. The best course in musicology is voice performance. A crucial issue is how to select and categorize valuable curriculum materials of varying quality levels. This paper implements an automatic classification and integration algorithm for vocal performance learning materials using machine learning technology and tests it on the corresponding dataset, beginning with multidimensional association rule mining technology and the premise that multimedia data contain numerous useful characteristics. Experiments demonstrate the classification precision and data integration capacity of the proposed algorithm. Vocal performance is the most engaging course in musicology. To cultivate corresponding talents, we can rely on college and university offline courses and the rapidly developing multimedia technology to train talents online. An important topic is how to efficiently select and classify curriculum materials of varying quality in order to provide them to students with different learning needs. We can achieve the automatic classification of the course content by leveraging the superior learning capabilities of artificial intelligence and machine learning technology. Consequently, this paper implements an automatic classification and integration algorithm for vocal performance learning materials using machine learning-related technology and conducts an experimental test on the corresponding dataset, beginning with multidimensional association rule mining technology and the perspective that multimedia information itself contains a large number of useful characteristics. Experiments demonstrate that the proposed algorithm has a high level of classification accuracy and data integration capability.

Applying Data Mining Approaches for Analyzing Hazardous Materials Transportation Accidents on Different Types of Roads

With the increase in the demand for and transportation of hazardous materials (Hazmat), frequent Hazmat road transport accidents, high death tolls and property damage have caused widespread societal concern. Therefore, it is necessary to carry out risk factor analysis of Hazmat transportation; predict the severity of accidents; and develop targeted, extensive and refined preventive measures to guarantee the safety of Hazmat road transportation. Based on the philosophy of graded risk management, this study used a priori algorithms in association rule mining (ARM) technology to analyze Hazmat transport accidents, using road types as classification criteria to find rules that had strong associations with property-damage-only (PDO) accidents and casualty (CAS) accidents under different road types. The results indicated that accidents involving PDO had a strong association with weather (WEA), traffic signals (TS), surface conditions (SC), fatigue (FAT) and vehicle safety status (VSS), and that accidents involving CAS had a strong association with VSS, equipment safety status (ESS), time of day (TOD) and WEA when urban roads were used for Hazmat transportation. Among Hazmat transport incidents on rural roads, the incidence of PDO accidents was associated with intersections (IN), SC, WEA, vehicle type (VT), and segment type (ST), while the occurrence of CAS accidents was associated with qualification (QUA), ESS, TS, VSS, SC, WEA, TOD, and month (MON). Strong associations between the occurrence of PDO accidents and related items, such as IN, SC, WEA and FAT, and the occurrence of CAS accidents and related items, such as ESS, TOD, VSS, WEA and SC, were identified for Hazmat road transport accidents on highways. The accident characteristics exemplified by strongly correlated rules were used as the input to the prediction model. Considering the scarcity of these events, four prediction models were selected to predict the severity of Hazmat accidents on each road type employing four analyses, and the most suitable prediction model was determined based on the evaluation criteria. The results showed that extreme gradient boosting (XGBoost) is preferable for predicting the severity of Hazmat accidents occurring on urban roads and highways, while nearest neighbor classification (NNC) is more suitable for predicting the severity of Hazmat accidents occurring on rural roads.

Human Resource Allocation Based on Fuzzy Data Mining Algorithm

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.

Association rule mining for the ordered placement of traditional Chinese medicine containers: An experimental study.

In traditional Chinese medicine (TCM) clinics, the pharmacists responsible for dispensing the herbal medicine usually find the desired ingredients based on positions of the shelves (racks; frames; stands). Generally, these containers are arranged in an alphabetical order depending on the herbal medicine they contain. However, certain related ingredients tend to be used together in many prescriptions, even though the containers may be stored far away from each other. This can cause problems, especially when there are many patients and/or the limited number of pharmacists. If the dispensing time takes longer, it is likely to impact the satisfaction of the patients’ experience. Moreover, the stamina of the pharmacists will be consumed quickly.In this study, we investigate on an association rule mining technology to improve efficiency in TCM dispensing based on the frequent pattern growth algorithm and try to identify which 2 or 3 herbal medicines will match together frequently in prescriptions. Furthermore, 3 experimental studies are conducted based on a dataset collected from a traditional Chinese medicine hospital. The dataset includes information for an entire year (2014), including 4 seasons and doctors. Afterward, a questionnaire on the usefulness of the extracted rules was administered to the pharmacists in the case hospital. The responses showed the mining results to be very valuable as a reference for the placement and ordering of the frames in the TCM pharmacies and drug stores.

Exploring the Correlation Between Attention and Cognitive Load Through Association Rule Mining by Using a Brainwave Sensing Headband

In recent years, Internet of Things (IoT) technology has brought many applications and developments for wearable devices, and the use of non-invasive electroencephalography (EEG) instruments to measure attention has been a topic of discussion. However, the correlation between attention and cognitive load has rarely been analyzed by data mining. For this reason, this study used head-mounted non-invasive EEG instruments based on IoT technology to collect attention values related to two courses and extracurricular activities and used a cognitive load questionnaire to investigate the cognitive loads of subjects. Correlation analysis was carried out through data mining technology to find the correlation between attention and cognitive load. In addition, six short-term experiments and relaxation experiments were designed to measure the subjects’ maximum attention and minimum attention values, so as to propose a strategy for setting the attention baseline. According to the results of the various experiments, subjects suffering from overload showed a state of inattention during the whole activity while subjects suffering a high load showed low sustained attention; only subjects with a medium load showed high sustained attention. Subjects with a low load showed inattention for nearly the entire activity. In this study, a strategy for setting an attention baseline was proposed to normalize the attention values from different EEG instruments. The correlation between attention value and cognitive load is analyzed using association rule mining technology so that the change of cognitive load could be effectively estimated by measuring the attention value instead of using questionnaire in the future.

Data Analysis of Soccer Athletes’ Physical Fitness Test Based on Multi-View Clustering

Data mining is a process that uses machine learning to extract valuable knowledge from a large number of data. The sportsman has a long period of exhaustion of human body motor function. The changes of physical function signals in each period are not obvious, there is a lack of dynamic association between the body function data, which makes the connection between the data not close. Traditional data mining methods are based on association rules mining technology [1], which excavates the most relevant data attributes in the data. Once the athletes’ body function data appear more obvious faults, the correlation is weakened, which will cause the deep excavation of the disease is inaccurate. In this paper, we take athletes’ different body function test data as different views of body performance, adopts a multi view clustering technology to analyse the association between test items, and further optimizes the physical fitness test index. The comprehensive evaluation of athletes based on physical data is transformed into a clustering problem, which can effectively solve the athletes’ physical status evaluation.

Research on Reliability Modeling of CNC System Based on Association Rule Mining

Lifetime failure data is often used in reliability modeling because of its advantage of convenient collection, but relatively large errors always existed when ignoring importance of failure correlation in reliability modeling for multiple failure positions and causes. Therefore, a reliability modeling based on degree of failure correlation was proposed, and failure correlation factor is introduced into parameter estimation part to fully reflect reliability information of lifetime failure data in reliability modeling. Then, using association rule mining technology, based on lifetime failure data to study failure correlation factor between failure positions and failure causes of CNC system. Finally, the study results show that the model which introduces failure correlation factor is suitable for modeling lifetime failure data of CNC system with multiple failure modes and causes.

Extracting Diagnosis Patterns in Electronic Medical Records using Association Rule Mining

Data mining technologies have been used extensively in the commercial retail sectors to extract data from their “big data” warehouses. In healthcare, data mining has been used as well in various aspects which we explore. The voluminous amounts of data generated by medical systems form a good basis for discovery of interesting patterns that may aid decision making and saving of lives not to mention reduction of costs in research work and possibly reduced morbidity prevalence. It is from this that we set out to implement a concept using association rule mining technology to find out any possible diagnostic associations that may have arisen in patients’ medical records spanning across multiple contacts of care. The dataset was obtained from Practice Fusion’s open research data that contained over 98,000 patient clinic visits from all American states. Using an implementation of the classical apriori algorithm, we were able to mine for patterns arising from medical diagnosis data. The diagnosis data was based on ICD-9 coding and this helped limit the set of possible diagnostic groups for the analysis. We then subjected the results to domain expert opinion. The panel of experts validated some of the most common associations that had a minimum confidence level of between 56-76% with a concurrence rate of 90% whereas others elicited debate amongst the medical practitioners. The results of our research showed that association rule mining can not only be used to confirm what is already known from health data in form of comorbidity patterns, but also generate some very interesting disease diagnosis associations that can provide a good starting point and room for further exploration through studies by medical researchers to explain the patterns that are seemingly unknown or peculiar in the concerned populations.

Application of Association Rule Mining Technology in Collection and Management of Wireless Sensor Network Node

Association rule mining is an important data mining method; it is the key link of finding frequent itemsets. The process of association rules mining is roughly into two steps: the first step is to find out from all the concentration of all the frequent itemsets; the second step is to obtain the association rules from frequent itemsets. This paper analyzes the collected information of nodes in wireless sensor network and management. The paper presents application of association rule mining technology in the collection and management of wireless sensor network node.

Study on Multidimensional Negative Association Rules

With the deepening of the negative association rules mining technology research, many key problems have been solved, but the solution of these problems are all on a single predicate in the transaction database. However, the data in the database often involves multiple predicates. This paper focuses on solving multi-dimensional support and confidence, negative association rules mining algorithm design problems. The experiment proves that the algorithm is correct and efficiency.

Research of Personalized Information Service Based on Association Rules

Association rules mining technology is a new data processing technology. Its algorithm and application play a very important role in the library. Obtaining personalized information of readers effectively and automatically is the key to carry out individualized service of university library. By using association rules technology, the library mine transaction data generated in the process of library service. And it also can have an access to various types of readers' information demand model, thus can provide accurate service for readers.

The Research and Application of Association Rules Algorithm

Through the research of the association rules mining technology and Apriori algorithm, the defects are found in Apriori algorithm. In view of the deficiencies, an improved algorithm is proposed. The algorithm scans database only once, and efficiently reduces the I/O time. The matrix of frequent itemsets is used to store and reduce the transaction data, which saves the storage space. By comparison of Apriori algorithm and improved algorithm, the results of experiments show that the efficiency of the improved algorithm is increased. Finally, an application example of the association rules is given. The improved algorithm is introduced to book lending deal. The rules among the book-borrowed are discovered and analyzed.

Experimental Research of Optimal Die-forging Technological Schemes Based on Orthogonal Plan

Some problems on setting up die-forging technological scheme design criteria by means of traditional methods are analyzed. The idea of mining die-forging technological schemes based on orthogonal plan is pointed out. The height and width of the hub are selected as key factors from many ones which influence the die-forging technological schemes of axisymmetric forging, then, in connection with the two factors, the relative experiments are arranged by using the two-factor-twice-composition orthogonal plan. Flash metal consumption is choosed from a lot of factors as object function whose values are measured in the experiments. When the height and width of the hub are the constant, from several experimental schemes, the technological scheme which makes flash metal consumption be the least is selected as the optimal scheme, so the design criteria of optimal die-forging technological schemes are got. Because of adopting orthogonal plan in arranging the experiments, the design and manufacturing period of forging is reduced, the developing cost of forging is cut down, and the raw and processed material consumption is decreased. In laying down the design criteria of optimal die-forging technological schemes, optimal technology is combined with artificial intelligence. In determining the relations among so many factors in die-forging technological schemes, expert’s experiences are used, and experimental results are dealt with by means of association rule mining technology, which makes the decision of die-forging schemes more reasonable and practical.