Traditional Apriori Algorithm Research Articles

Path analysis of digitally empowered mental health services for university students

Abstract We investigate the viability of using data mining technologies to identify college students’ mental health issues in light of the rising number of these issues. To address the limitations of the traditional Apriori algorithm in data mining of mental health problems, an improved Apriori algorithm is proposed using the classification rule mining method. The relationship between various factors and the mental health problems of college students is better explored by this algorithm. Ultimately, the mental health care pathway that was developed during the exploration was used to conduct a comparative trial between those who received mental health services and those who did not. The experimental group’s mean score in the hyperactive concentration inability dimension was 3.25 after getting mental health care for three weeks, which was 22.6% higher than the control group’s mean score. The aspects of emotional symptoms, pro-social conduct, and total difficulty score also showed significant variations (p<0.05). The mean scores of the experimental group in the pro-social behavior and emotional symptoms dimension in the course of the study and the pre-experiment were 2.17 and 0.57, accordingly, which both demonstrated highly significant differences (p<0.01) in the between-group comparison of the differences in scores at different evaluation times with the control group.

Digital innovations in higher education management and mechanisms for high-quality training of students

Abstract With the rapid development of modern information technology, the management mode and cultivation path of higher education have undergone great changes, and this paper explores this change from digital innovation. First of all, this paper analyzes the mechanism of education management and student training in higher education and puts forward the digital innovation path of higher education management and student training in combination with education big data. Then, on the basis of the traditional Apriori algorithm, the AHP-DEMATEL method is introduced for empowerment, and weighted ordered directed constraint Apriori algorithm is proposed for data mining, and the PSO-LS-SVM prediction model is proposed for data prediction by combining particle swarm algorithm for optimization under least squares support vector machine algorithm. Finally, the data on educational management and the effects of student cultivation and innovation on students are mined and predicted, and the relationship between the two is explored. The results show that the improved Apriori algorithm ends up with the number of rules around 20 when the support degree is 0.22, the confidence degree is 0.50, and the interest degree is 0.80, and the relative errors of the prediction are all under the level of 0.03, and the average relative error of the data of the experimental results is 78, and the innovation scores are all above 80. Higher education management and the digital innovation of students’ high-quality training mechanisms show a positive correlation, which can promote the overall development of students.

Research on key risk chain mining method for urban rail transit operations: A new approach to risk management

To ensure the safety of urban rail transit operations and uncover the transmission dynamics of risk sources, a key risk chain mining method for urban rail transit operation is proposed. Firstly, the H-Apriori association rule algorithm is proposed for the characteristics of low frequency but high riskiness of high hazard degree risk sources in urban rail transit operation, which adds a new hazard degree evaluation index to the traditional Apriori algorithm and couples with support degree two-dimensionally to mine the strong association rules among risk sources. Secondly, we construct a weighted risk network with risk sources as network nodes and strong association rules as network edges, and propose a key risk chain mining method for urban rail transit operation based on path search theory to mine key risk chains from the weighted risk network. Finally, using the actual urban rail transit operation data of a city in China as an example, a total of 17 key risk chains are mined, and then 5 key risk sources and 8 key chain break locations are obtained by riskiness and frequency analysis of key risk chains, and control plans are proposed. The research outcomes introduce a novel approach to mining risk chains in urban rail transit operations, shedding light on the propagation mechanisms, triggering probabilities, and degrees of unsafety associated with risk sources. The results not only provide theoretical support but also offer methodological guidance for pinpointing locations of risk chain breaks and refining the control of risk sources.

Hidden Danger Association Mining for Water Conservancy Projects Based on Task Scenario-Driven

With the rapid development of water conservancy engineering and infrastructure construction, there are many safety hazards in the construction process of water conservancy engineering, so it is of great significance to study the potential hazards in the construction process. In this context, this paper proposes a task scenario-based association mining method for hydraulic engineering hidden danger records. By analyzing transaction characteristics, the traditional Apriori algorithm is improved to optimize pruning results and generate hidden danger association rules. The research results of this paper have been successfully applied to the investigation and management of hidden dangers in the Xinmenghe dredging project. Based on the mapping of association rules driven by task scenarios, hidden dangers association rules in specific task scenarios are mined to assist construction safety managers in hidden dangers investigation, which reduces the complexity of the algorithm, reduces the running time of the algorithm and improves the efficiency of the algorithm.

WOMDI-Apriori Data Mining Algorithm for Clustered Indicators Analysis of Specialty Groups in Higher Vocational Colleges

The cluster effect of specialty groups plays an important role in the development of Higher Vocational Colleges. The purpose of this research is to scientifically explore the interaction mech- anism of specialty groups clustering indexes in higher vocational colleges, uantitatively analyze the correlation of these indexes, nd explore reasonable measures to promote the specialty groups clustering effect in higher ocational olleges. Firstly, data denoising and field screening were car- ried out on the original data, and then the variables were clustered and divided into LHS (Left Hand Side) and RHS (Right Hand Side). Then, an improved multi-dimensional interactive Apri- ori association rule mining algorithm considering index weights and orientation constraints was proposed. The improved Apriori algorithm and the traditional Apriori algorithm were applied to mine the structured data sets. The results show that the improved WOMDI-Apriori algorithm in this study improves the accuracy by 79.96% compared with the traditional Apriori algorithm. The results indicate that, when the indicators of brand, key and characteristic majors at or above the provincial level, proportion of full-time teachers with double qualifications, and the number of internship students accepted by cooperative enterprises are at a low level, the number of projects and satisfaction proportion of employers with graduates would be negatively affected; The majr category of equipment manufacturing is subjected to various factors coupling, which may lead to different graduates’ counterpart mployment rate; for association rules where the uccessor of the mining results is dominated by negative results, measures should be taken to avoid or reduce the possibility of their occurrence as much as possible. For association rules in which the successors of the mining results are dominated by positive results, measures should be taken to facilitate the occurrence of these frequent item sets whenever possible. The framework proposed in this research can provide theoretical guidance for analyzing operating characteristics and promoting the positive effects of specialty groups in higher vocational colleges.

Using Data Mining Techniques to Diagnosis of the Covid-19 Effects on the Hospital Readmission

The COVID-19 pandemic led to a substantial increase in the volume, diversity, and output pace of healthcare data. Countries depended on traditional methods to monitor diseases and public health to manage the epidemic, while advanced technology such as artificial intelligence and computation enabled efficient data processing. That datasets are usually enormous, growing exponentially, and comprise a collection of complicated item sets. To extract big, complicated itemsets, robust, straightforward, and computationally efficient techniques are crucial. Based on concepts from computer science, machine learning, and data mining, the Apriori method is a viable approach for supporting the values of database items in this study. There are two distinct implementation methods for Apiori: low confidence and support (Apiori algorithm) and the Apriori property algorithm. In conclusion, the performance of the Apriori property algorithm was superior to that of the traditional Apriori algorithm.

Load combination feedback of fracture in concrete dams based on monitoring data with simplified fuzzy association rules

The existed cracks are serious potential threats to the integrity and safety of concrete dams, and may even lead to dam failure and flood disaster. The fracture behavior in concrete dams is influenced by a kaleidoscope of load effects, which act in combination with a complicated relationship. A quantitative analysis method with improved association rule mining (ARM) based on monitored crack opening is proposed in this study. Fuzzy Set theory and Fuzzy C-Means method are introduced to establish the fuzzy ARM method for sequential monitoring data, and the traditional Apriori algorithm is simplified based on the single decision attribute. The feasibility and efficiency of the proposed method is validated by its application in Chencun gravity arch dam with a large structural fracture, and low temperature and low upstream water level are identified as the detrimental load combination for propagation of the fracture in this dam.

Research on data resource management of biomass energy engineering based on data mining

In order to improve the information management level of biomass energy industry, it is necessary to build and improve the data resource management platform of biomass energy engineering, so as to grasp the detailed operation status and production situation of biomass energy engineering in real time. In this paper, the data mining concept and technology based on Hadoop are applied to the construction of data resource management platform of biomass energy engineering, which solves the difficult problems of mass data collection, storage, processing and analysis of biomass energy. The bottleneck of Apriori algorithm in processing big data lies in the high time complexity. Aiming at this shortcoming of Apriori, this paper combines Apriori algorithm with Hadoop framework, and combine Apriori algorithm with Hadoop framework, and test the data mining of Apriori algorithm after combination. Results The test realized the optimal design of storage mode of massive database, and the performance of Apriori algorithm was improved by 40% compared with traditional Apriori algorithm.

Research on Product Recommendation Based on Association Rules and Improved Apriori Algorithm

With the rapid development of the Internet era, online sales services have gradually become an indispensable part of people's daily life. In the face of huge network data, how to find products that can maximize the utility of customers and form product recommendations that are beneficial to merchants has become an important issue. In this paper, through the in-depth analysis of the commodities and customer purchase data of a bicycle commodity store, the Apriori algorithm is used to mine the association rules of the data, and the commodity combination with strong correlation is obtained. At the same time, the traditional Apriori algorithm has certain limitations, that is, the algorithm only considers the probability of the transaction, but does not consider the weight of different products, and then we introduce the intuitionistic fuzzy number to represent the weight of the item. Improvements have been made, resulting in a more accurate and effective product recommendation combination. The implementation of this algorithm can be widely used in the commercial field to achieve the purpose of using best-selling commodities to drive relatively non-selling commodities, and at the same time maximize the utility of consumers, which also brings greater benefits to businesses, thus forming a healthy network. The sales structure has important practical significance to the current era.

Application of Apriori Improvement Algorithm in Asthma Case Data Mining.

In Chinese medicine, asthma cases contain a large amount of empirical data which are obtained from the clinical diagnosis of doctors throughout the year. Data correlation analysis method is among the common mechanisms which are used to mine association between the (1) prescriptions and prescribers (doctors in this case) and (2) symptoms and medications for a particular disease in the hospitals. In this paper, initially, a thorough analysis of expected performance and shortcomings of the Apriori algorithm in mining of medical case data is presented. Secondly, we propose an extended version of the traditional Apriori algorithm which is primarily based on the fast response of computer to bit-string logic operation. A comparative evaluation of the proposed and existing Apriori algorithms is presented particularly in terms of running time, mining of frequent items set and strong association rules. Both experimental and simulation results have proved that the proposed extended Apriori algorithm has outperformed existing algorithms when it is applied to asthma medication and combined symptom-medication data for the association analysis. Furthermore, the association relationship between mind asthma case data and medication is effective in the analysis of asthma case data with significant application value which is verified by the experimental data and observations.

Data Mining and Risk Prediction Based on Apriori Improved Algorithm for Lung Cancer

Starting from medical big data, this article uses data mining technology to analyze and study the pathogenic factors of lung cancer based on the lung cancer electronic medical record data from the oncology department of the authoritative third grade A hospital for many years. With respect to the processing of huge data from electronic medical records for lung cancer, traditional serial Apriori algorithm has the disadvantages of scanning database frequently, running slowly and consuming large amount of memory resources. Therefore, an improved Apriori algorithm based on MapReduce distributed computing model of Hadoop platform is proposed. The experimental cluster and lung cancer data mining experiments show that the improved Apriori algorithm has higher execution efficiency and good system scalability in dealing with lung cancer big data, and can well mine the relationship between lung cancer and pathogenic factors, which has important guiding significance for assisting the clinical diagnosis and risk prediction of lung cancer.

Analysis of Tumor Disease Patterns Based on Medical Big Data

Using the medical big data mining related technology, the model of tumor disease was analyzed and studied. Using data science methods as a guiding method and idea, analyzing and constructing a medical service model based on big data for oncology diseases, exploring its development strategy; using business process analysis method to analyze the business process and mapping of cancer disease medical services; using serviceoriented architecture analysis and Design methodology to build a highly flexible, configurable, and easily scalable precision medical big data platform. By analyzing the characteristics of medical big data and the shortcomings of the traditional Apriori algorithm, the Hadoop platform is used to improve and optimize the Apriori algorithm. The results show that the improved Apriori algorithm has great improvement in efficiency and performance, and can be adapted to mining medical big data. Through data mining experiments, it is concluded that there is a correlation between tumors and smoking, chronic infection, occupational pathogenic factors, etc. It has certain guiding significance for the prevention and treatment of tumors, thus also demonstrating the improved Apriori algorithm for lung tumors. Clinical research has practical significance.

Research on parallelization of Apriori algorithm in association rule mining

Abstract Aiming at the performance bottleneck of traditional Apriori algorithm when the data set is slightly large, this paper adopts the idea of parallelization and improves the Apriori algorithm based on MapReduce model. Firstly, the local frequent itemsets on each sub node in the cluster are calculated, then all the local frequent itemsets are merged into the global candidate itemsets, and finally, the frequent itemsets that meet the conditions are filtered according to the minimum support threshold. The advantage of the improved algorithm is that it only needs to scan the transaction database twice and calculate the frequent item set in parallel, which improves the efficiency of the algorithm.

Map Reduce Based Association Rule Mining from Big Data

In today’s world, the shopping is the largest fashionable trend where the transaction processing is meticulous to fetch the items from the shopping transaction history by using traditional Apriori algorithm. An Apriori algorithm is the one which is used for finding frequent pattern from the given dataset. The problem of Apriori is to find useful itemsets for business purpose was time consuming. To overcome this problem, we have proposed Map Reduce based Apriori algorithm which generates frequent itemset and association rules by using parallel computations to reduce computations. The Spark distributed systems along with data bricks technology have been used. The experimental result shows that have been reduced the time taken fetch the data from the database.

Blind identification of network protocols based on improved Apriori algorithm

With the development and application of network technology and private protocols, more and more unknown protocols are found in communication networks. Analysis and identification of unknown protocols has become an important and difficult problem in obtaining valuable information from data. Based on the traditional Apriori algorithm, a reduced bit string algorithm and a weight compression algorithm are proposed to identify the unknown protocols according to the characteristics of network unknown protocol data in this paper. These proposed algorithms make use of location information, matrix compression, bit string and weight compression to gradually improve the identification accuracy and the processing efficiency of unknown protocols. Experiments on simulation data and actual data demonstrate the performance of the improved algorithms in terms of identification accuracy and time efficiency.

WITHDRAWN: Video structure syntax mining based on Hidden Markov Model in sports video

Abstract On the basis of shot segmentation, an improved Rate Sensitive Competitive Learning algorithm (RSCLA) is proposed for unsupervised shot clustering based on NBA basketball video, which converts video stream data into symbol sequence. Aiming at the characteristics of sequence correlation, time correlation and no clear transaction concept in Video Association rules, this paper improved the traditional Apriori algorithm and proposed a video Association Rules Mining Algorithm based on time-based window computing support. The periodic or semi-periodic structural grammar patterns in video are explored with frequent sets of association rules to guide the establishment of video structure semantic mining model. Aiming at the limitation of string matching, this paper proposes a HMM-based pattern mining method to analyze high-level video events, identify and locate free throw events in basketball videos. The experimental results show that the four-state HMM is the inner free throw mode of basketball video and achieves good performance.

Analysis of Sales Pattern Determination System and Drug Stock Recommendation

The tight competition in the pharmacy industry, requires pharmacy owners to develop strategies in increasing drug sales. One of the strategies carried out is to analyze patterns of drug sales and determine drug stock recommendations based on sales transaction data. Based on this, an application was built to determine the pattern of drug sales and drug stock recommendations by using a modified Apriori Algorithm and Triple Exponential Smoothing Method. Apriori algorithm modification is used to overcome the problem of large amounts of sales transaction data, thus minimizing the time in the database scan process. Triple Exponential Smoothing method is used in determining drug stock recommendations based on sales patterns that have been generated to prevent excess or lack of stock. Application testing techniques used are performance testing, lift ratio and accuracy testing. The research resulted in a sales pattern that has a strong association rule and time analysis using Apriori Algorithm modification that is faster than using a traditional Apriori Algorithm and the percentage error value of drug stock recommendations by 31.84%.
 Keywords: Sales Pattern, Stock Recommendations, Apriori Algorithm Modification and Triple Exponential Smoothing

Application of improved time series Apriori algorithm by frequent itemsets in association rule data mining based on temporal constraint

The basic idea of Apriori algorithm is first introduced in this paper, which is to find all frequent sets in a transaction. The frequent requirements of these frequent sets are greater than or equal to the minimum support of the set. On this basis, the working principle of the traditional Apriori algorithm is analyzed, and the existing problems are pointed out. To solve these problems, an improved Apriori algorithm is proposed for time series of frequent itemsets. Finally, on the basis of analyzing the methods and processes of mining association rules for time series, this improved time series Apriori algorithm for frequent itemsets is applied to mining association rules based on time constraints. The experimental results show that the improved Apriori algorithm is better than the traditional one in storage space.

Research on sensor network optimization based on improved Apriori algorithm

Traditional node deployment strategies have problems such as slow deployment, small coverage, and poor service quality. Based on this, this study improves the traditional Apriori. This study uses the grid overlay model as the basic model and applies the improved Apriori algorithm to the WSN (wireless sensor networks) coverage optimization process of mobile nodes. This study simulates from different population sized SizePops, different search interval lengths L, and different maximum iterations Maxgen. At the same time, this paper selects the traditional Apriori algorithm and the improved Apriori optimization algorithm to compare and analyze. The research shows that the improved Apriori optimization algorithm can better combine the wireless sensor network model of the mobile node and can obtain higher network coverage under the same parameters, and the problem can be solved more effectively.

MCRS: A course recommendation system for MOOCs

With the popularization development of MOOC platform, the number of online courses grows rapidly. Efficient and appropriate course recommendation can improve learning efficiency. Traditional recommendation system is applied to the closed educational environment in which the quantity of courses and users is relatively stable. Recommendation model and algorithm cannot directly be applied to MOOC platform efficiently. With the light of the characteristics of MOOC platform, MCRS proposed in this paper has made great improvement in the course recommendation model and recommendation algorithm. MCRS is based on distributed computation framework. The basic algorithm of MCRS is distributed association rules mining algorithm, which based on the improvement of Apriori algorithm. In addition, it is useful to mine the hidden courses rules in course enrollment data. Firstly, the data is pre-processed into a standard form by Hadoop. It aims to improve the efficiency of the basic algorithm. Then it mines association rules of the standard data by Spark. Consequently, course recommendation information is transferred into MySQL through Sqoop, which makes timely feedback and improves user’s courses retrieval efficiency. Finally, to validate the efficiency of MCRS, a series of experiments are carried out on Hadoop and Spark, and the results shows that MCRS is more efficient than traditional Apriori algorithm and Apriori algorithm based on Hadoop, and the MCRS is suitable for current MOOC platform.