Traditional Association Rule Algorithm Research Articles

Artificial Intelligence Technology Facilitates Model Innovation in Higher Education Management and Student Training Mechanisms

Abstract In order to promote the model innovation of higher education management and student training mechanisms, this paper proposes data mining technology to assist the research of education management and training mechanisms. Using web crawler technology to obtain the required initial data for the study, data cleaning and conversion are carried out to ensure the accuracy of the results of the research and analysis in response to the problems of missing data, intermingled noise, and attribute redundancy in the initial data. Aiming at the slow efficiency and collapse of the traditional association rule algorithm in the face of massive data, the whale optimization algorithm is used to optimize the association rule algorithm for multi-objective optimization, and the optimized association rule algorithm is used to analyze the data of higher education management. The data showed that the Confidence=77.82% for the association rule poor grades in introductory courses=>poor grades in major courses, which indicates that the probability of students whose average grades in introductory courses are outside the 75th percentile and those whose average grades in significant classes are outside the 60.00th percentile is 77.82%. This study aids teachers and administrators in making scientific decisions to enhance teaching management, enhance teaching quality, and reduce costs associated with school operations.

Route planning model based on multidimensional eigenvector processing in vehicular fog computing

With the rapid development of information technology, the informationization level of sport tourism has been improved in an all-around way, which makes a large number of data accumulated in the management system of road traffic. However, the traditional association rule algorithm cannot deal with huge data. To make up for the deficiency of predicting suitability from a single perspective, this paper, from the perspective of road daily travel planning, constructs a multi-dimensional characteristic model. Based on eigenvector processing as a research content, by carrying out a multi-dimensional prediction on the whole data set and considering the limitation problem of the influence of time, space, and items on daily travel routes, the invention provides a daily travel suitability road daily travel planning route prediction research method based on multiple dimensions. The invention also extracts the outstanding performance of a network model in a single variable problem based on deep interest and double characteristics. The combination of SVR and GBRT algorithm makes up for the one-sidedness of single perspective prediction. It uses the weighted fusion principle to fuse the results and establishes a multi-dimensional route suitability prediction model under this mode. Experiments verify that the prediction of multivariate dimensional data achieves the desired results. With the help of the dependence between the level and element dimension data, the future route planning trend can be judged. This algorithm compared to the ant colony algorithm and the traditional genetic algorithm increased by 15.6% and 15.1%. The system response time has been increased by more than 60%, which can effectively improve the accuracy of prediction. Therefore, in the VFC environment, the actual user needs can be improved, the planning and management of traffic routes can be guided, and the development of sport tourism systems of Taihang Mountain can be further promoted.

Neutrosophic Fuzzy Association Rule Generation-Based Big Data Mining Analysis Algorithm

As a very common and classic big data (BD) mining algorithm, the association rule data mining (DM) algorithm is often used to determine the internal correlation between different items and set a certain threshold to determine the size of the correlation. However, the traditional association rule algorithm is more suitable for establishing Boolean association rules between different items of different types of data, and hardening the sharp boundaries of the data causes the performance of the association rules to decrease. In order to overcome this shortcoming of classic DM, this article introduces association rules, support and confidence, the Apriori algorithm and fuzzy association rules based on the neutrosophic fuzzy association rule (NFAR). This paper is based on the data set of the supermarket purchase goods database, by drawing a radar chart to describe the correlation between different goods and different item sets support, and confidence calculation based on association rules support. Finally, the association rules are generated. Compared to the results produced by NFAR and ordinary association rules, the accuracy of the NFAR association rules algorithm in small data sets is 88.48%, while the accuracy of traditional association rules algorithm is only 80.87%, nearly 8 percentage points higher. On large data sets, the prediction accuracy of the neutral fuzzy association rules algorithm is 95.68%, while that of the traditional method is only 89.63%. Therefore, the NFAR algorithm can improve the accuracy and effectiveness of DM. This algorithm has great application prospects and development space in big DM and analysis.

Discussion on Redundant Processing Algorithm of Association Rules Based on Hypergraph in Data Mining

With the rapid advancement of big data, it is becoming a great problem for people to find objective information in the database. The relevance data processing rule for digging the information can be the way. Relevance data processing rule for digging the information is mainly studied in three aspects: data dimension, data abstraction level, and processing variable type. In the aspect of rules, the research mainly focuses on three aspects: active relationships, passive rules, and uncommon relationship rules. Association rules of digging the data can be the most well-employed investigation goal and aim for the data digging. Along with the advancement of the information scale, the time rate of traditional relationship rules exploration of counting ways is too low. How to increase the time rate for the way of counting is the main research content of relevance data processing rules for digging the information. Current relevance data processing rules for digging the information have two limitations: (1) metrics such as support, confidence, and lift rely too much on expert knowledge or complex adjustment processes in value selection; (2) it is often difficult to explain rare association rules. Based on the existing research, this paper proposes a Markov logic network framework model of association rules to address the above shortcomings. The theory of a hypergraph and system is proposed, and the method of a hypergraph in 3D matrix modeling is studied. Aiming at the new characteristics of big data analysis, a new super edge definition method is introduced according to the definition of the system, which greatly enhances the ability to solve problems. In the cluster analysis and calculation of hypergraph, this paper will use the hypergraph segmentation operator hMETIS to carry out the cluster analysis method in order to achieve higher accuracy in cluster analysis and calculation. As for the test of cycle ones, which is in line with the relevance of the hypergraph with clear directions, the thesis will offer a brand-new way to make an analysis and turn the rule of relevance into the hypergraph with clear directions with a new definition of the near linking matrix, and it will change the dealing way from the test of the cycle and more ones into the linking f bricks and circles, which is a new way to explore. This paper uses two datasets of different sizes to conduct rule prediction accuracy experiments on the Markov logic network framework model algorithm of association rules and the traditional association rule algorithm. The results show that compared with the traditional association rule algorithm, the rules obtained by the Markov logic network framework model of association rules have a higher prediction accuracy.

Association Rule Model of On-demand Lending Recommendation for University Library

University library which is connected with the Internet is more convenient to search, but the huge amount of data is not convenient for users who lack precise target. In this study, the traditional association rule algorithm was improved by Bayesian algorithm, and then simulation experiment was carried out taking borrowing records of 1000 students as examples. In order to verify the effectiveness of the improved algorithm, it was compared with the traditional association rule algorithm and collaborative filtering algorithm. The results showed that the recommendation results of the improved association rule recommendation algorithm were more relevant to students’ majors, and the coincidence degree of different students was low. In the objective evaluation of the performance of the algorithm, the accuracy, recall rate and F value showed that the personalized recommendation performance of the improved association rule algorithm was better and the improved association rule algorithm could recommend users with the book type that they need.

Mining patterns of Chinese medicinal prescription for diabetes mellitus based on therapeutic effect

Traditional Chinese medicine (TCM) prescription comprises groups of Chinese herbs that embody thousands of years of history with respect to the treatment of diabetes mellitus (DM), a condition for which there are numerous prescriptions with different therapeutic effects. Existing studies on prescription patterns are based on the frequencies calculated using the traditional association rule algorithm. However, the most important concern for physicians is the efficacy of drug combinations in clinical practice, as no existing study has considered the efficacy of prescriptions. In this study, a weighted association rule algorithm called MWFPP (Mining Weighted Frequent Patterns of Prescription) was used to mine and analyze TCM prescriptions for the treatment of DM based on the therapeutic effect. As a result, the ranking of drug combinations with a low frequency but the good therapeutic effect in the randomized controlled trials (RCT) increased. These drug combinations were also effective in the treatment of DM according to TCM theory. Hence, effective drug combinations could be promising for prescription compatibility in clinical practice and drug discovery.

Particle Swarm Optimization-Based Association Rule Mining in Big Data Environment

With the explosive growth of information data in today's society, the continuous accumulation and increase of data in recent years make it difficult to extract useful information from it, so data mining comes into being. Association rule mining is an important part of data mining technology. Association rule mining is the discovery of frequent item sets in a large amount of data and the mining of strong association relations between them. Traditional association rule algorithms need to set minimum support and minimum confidence in advance. However, these two values are largely influenced by human subjectivity. Many scholars use average and weight to set these two values, but the effect is still not very good. In order to solve this problem, this paper proposed an improved algorithm of association rules - PSOFP growth algorithm, this algorithm is introduced into intelligent algorithm, particle swarm optimization algorithm, it can find the global optimal solution, we use this fact to find the optimal support, then using FP - growth algorithm for mining association rules, and finally put forward by information entropy to measure effectiveness in association rules mining, and the improved algorithm was applied to the social security event correlation analysis, the improved algorithm proved to our expectations.

An Incremental Association Rule Algorithm Based on MapReduce

Association rules are one of the important methods of Data Mining, but the traditional association rule algorithm requires multiple scans of the database, with high I/O overhead and failure to handle node failure and load balancing. MapReduce is a popular distributed parallel computing model with good scalability and automatic load balancing and has been widely used. The existing algorithm for mining parallel association rules based on MapReduce computing model improves the computational efficiency. However, existing studies have ignored the characteristics of data increments. This paper will combine MapReduce and incremental data, and then develop and design incremental association rules based on MapReduce and combine the mathematical theory to design the mining algorithm. The effectiveness of the algorithm is verified through experiments.

P-CSREC: A New Approach for Personalized Cloud Service Recommendation

It is becoming a challenging issue for users to choose a satisfied service to fit their need due to the rapid growing number of cloud services and the vast amount of service type varieties. This paper proposes an effective cloud service recommendation approach, named personalized cloud service recommendation (P-CSREC), based on the characterization of heterogeneous information network, the use of association rule mining, and the modeling and clustering of user interests. First, a similarity measure is defined to improve the average similarity (AvgSim) measure by the inclusion of the subjective evaluation of users’ interests. Based on the improved AvgSim, a new model for measuring the user interest is established. Second, the traditional K-Harmonic Means (KHM) clustering algorithm is improved by means of involving multi meta-paths to avoid the convergence of local optimum. Then, a frequent pattern growth (FP-Growth) association rules algorithm is proposed to address the issue and the limitation of traditional association rule algorithms to offer personalization in recommendation. A new method to define a support value of nodes is developed using the weight of user’s score. In addition, a multi-level FP-Tree is defined based on the multi-level association rules theory to extract the relationship in higher level. Finally, a combined user interest with the improved KHM clustering algorithm and the improved FP-Growth algorithm is provided to improve accuracy of cloud services recommendation to target users. The experimental results demonstrated the effectiveness of the proposed approach in improving the computational efficiency and recommendation accuracy.

An Algorithm of Association Rule Based on Cloud Computing

Large-scale data processing is one of the focal points of research in information technology. The traditional al- gorithm of association rule is in a large overhead, due to the frequent itemsets being computed on the dataset. The rapid development of distributed technology makes cloud computing a reality in the implementation of data processing algo- rithms. To improve the traditional association rule algorithm, in this paper, an AprioriMR algorithm for mining associa- tion rule based on cloud computing is proposed. The AprioriMR algorithm takes HDFS to store data and is well adapted to the Hadoop's Map-Reduce computing model. It divides into two parts, deals with Map-Reduce operation, and combines to produce the frequent patterns. The AprioriMR algorithm inherits the Map-Reduce scalability to huge datasets and to thousands of processing nodes. Experimental results show that it is very efficiently compared with the traditional associa- tion rules algorithm and has a good speedup when deals with massive data.

Research of Association Rules Algorithm Based on Matrix under Cloud Computing

It occurs time and space performance bottlenecks when traditional association rules algorithms are used to big data mining. This paper proposes a parallel algorithm based on matrix under cloud computing to improve Apriori algorithm. The algorithm uses binary matrix to store transaction data, uses matrix "and" operation to replace the connection between itemsets and combines cloud computing technology to implement the parallel mining for frequent itemsets. Under different conditions, the simulation shows it improves the efficiency, solves the performance bottleneck problem and can be widely used in big data mining with strong scalability and stability.

Finding association rules in semantic web data

The amount of ontologies and semantic annotations available on the Web is constantly growing. This new type of complex and heterogeneous graph-structured data raises new challenges for the data mining community. In this paper, we present a novel method for mining association rules from semantic instance data repositories expressed in RDF/(S) and OWL. We take advantage of the schema-level (i.e. Tbox) knowledge encoded in the ontology to derive appropriate transactions which will later feed traditional association rules algorithms. This process is guided by the analyst requirements, expressed in the form of query patterns. Initial experiments performed on semantic data of a biomedical application show the usefulness and efficiency of the approach.