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Each student in an adaptive education system has significant differences in knowledge background, ability level and cognitive style. Therefore, to build an adaptive teaching system, it is necessary to establish an operable, reasonable and individualized student model by clarifying students’ abilities and differences. The improved Apriori algorithm under big data is the most classic association rule algorithm, which is generated by a set of candidates, and it uses the iterative method of hierarchical search to traverse a set of frequency items in the transaction database. After finding the set of frequency items, select the association according to the trust rules. This paper studies how to apply the improved Apriori algorithm to an adaptive online education system in a big data environment. An evolutionary algebra is taken with mean fit of 80, population size of 20, mean fit of 0.28, population size of 60, mean fit of 0.26, population size of 80, mean fit of 0.25, mean population size of 0.25. The population size is 200, and the average fitting is 0.24. The larger the error, the smaller the error between each indicator of the test paper and the corresponding value specified by the user. The improved Apriori algorithm in the big data environment has designed five themes of rule mining, which are mainly used for class management: class linkage, class category linkage, student basic information linkage, lecture and basic information linkage, and lecture mode linkage. They play the role of teaching assistants with a specific role.

With the advent of the big data era, data-driven decision-making and analysis are increasingly valued in various fields. Especially in the field of education, how to use big data technology to better understand student needs, optimize the education process, and improve education quality has become an important research topic. This paper will explore the application of decision trees and related analysis algorithms in the analysis of college students’ physical fitness, in order to provide scientific basis for improving the physical health level of college students. This paper studies the application of DT (decision tree) and correlation analysis algorithm in the analysis of college students’ physical fitness. In this paper, the method of big data and DM (data mining) is proposed to extract the rules contained in the data information, so as to directly provide auxiliary decision-making for physical fitness test and analysis. The research results show that through training the training set, a good classification accuracy rate is achieved, and through optimizing the depth, the accuracy rate can reach more than 85.033%. Using DM technology as a carrier, this paper digs into the rules behind the new knowledge of college students’ physical fitness, and digs out the previously unknown, implied and potentially useful information and knowledge.

As the advancement of network technologies, the recruitment industry is also showing a trend of networking, but the current online recruitment lacks the application of data mining (DM) technology, and its analysis of data is limited to recruitment websites. Therefore, the study proposes a DM-based online recruitment technology that selects the best career candidate through correlation analysis of social media data. The study uses Scrapy crawler to obtain data and utilises an improved Apriori algorithm for correlation analysis. The research findings denote that the proposed algorithm has excellent convergence performance and training efficiency. The study is of experimental design type using experimental data for analysis. In contrast with the traditional Apriori and FP-growth algorithms, the fitting of the output results increases by 6.21% and 14.67%. In addition, the improved algorithm shows significant optimisation effects, with an average running time reduced by 2.44 s and 0.76 s, respectively, compared with the two algorithms, and is less affected by the minimum confidence level. In fit testing, the average error of this method is only 0.02. In summary, online recruitment technology based on DM has strong availability and high reliability. The improved algorithm has excellent performance, accurate output results, and can accurately apply data from social media to select the best job candidate.

Worldwide, epidemics continue to be a concern on public health. Even with the technological advances, there are still barriers present in predicting the outbreaks. We propose a new methodology known as FREEDOM (Effective Surveillance and Investigation of Water-borne Diseases from data-centric networking using Machine Learning) to perform effective surveillance and investigation of water-borne diseases from social media with next-generation data. In the proposed model, we collected the data from the Twitter media, preprocessed the tweet content, performed hierarchical spectral clustering, and generated the frequent word set from each cluster through the apriori algorithm. At last, the inferences are extracted from the frequent word set through human intervention. From the experimental results, the support and confidence value of the outcome derived from the Apriori algorithm has exhibited the different water-borne diseases that are not listed in the WHO (World Health Organization), and the surveillance of those diseases with percentage ranking and has been achieved using the data-centric networking. They get aligned with precise results portraying real statistics. This type of analysis will empower doctors and health organizations (Government sector) to keep track of the water-borne diseases, their symptoms for early detection, and safe recovery thereby sufficiently reducing the death tolls.

Bug triaging is a process to decide what to do with newly coming bug reports. In this paper, we have mined association rules for the prediction of bug assignee of a newly reported bug using different bug attributes, namely, severity, priority, component and operating system. To deal with the problem of large data sets, we have taken subsets of data set by dividing the large data set using $K$-means clustering algorithm. We have used an Apriori algorithm in MATLAB to generate association rules. We have extracted the association rules for top 5 assignees in each cluster. The proposed method has been empirically validated on 14,696 bug reports of Mozilla open source software project, namely, Seamonkey, Firefox and Bugzilla. In our approach, we observe that taking on these attributes (severity, priority, component and operating system) as antecedents, essential rules are more than redundant rules, whereas in [M. Sharma and V. B. Singh, Clustering-based association rule mining for bug assignee prediction, Int. J. Business Intell. Data Mining11(2) (2017) 130–150.] essential rules are less than redundant rules in every cluster. The proposed method provides an improvement over the existing techniques for bug assignment problem.

This paper reports a basic assessment of the usage of a knowledge repository called "XYZ Knowledge Repository", maintained by a company PQR for its client XYZ. Subsequently, a data mining method for finding sequential patterns in the web usage of the users was used. This captures the level of association between the web pages through two parameters, support and confidence. Depending upon the values of these two parameters, concepts of strong association, weak association and no association are realised. In case of strong association we recommended the pages be grouped together; while in the case of weak association, a link may be provided.

The quick development of intelligent information technology in modern society has accelerated education informatisation. Accordingly, the traditional modes of instruction and methods of instruction are no longer able to satisfy the educational needs of modern society, so the flipped classroom teaching method emerges at the historic moment. While injecting fresh vitality into education, this model can also reflect good teaching effects, which is of enormous practical relevance to the development of education. Additionally, flipped classroom teaching is made possible by the online teaching resources offered by colleges and institutions. In view of the unreasonable distribution of curriculum resources in the current university network teaching platform, this study proposes a university network platform combining flipped classroom teaching mode and improved Apriori algorithm. First of all, to achieve the acceptable allocation and suggestion of learning materials in accordance with students’ learning demands, the Apriori algorithm is introduced to address the university network teaching platform’s lack of adequate planning and organisation. After that, the flipped classroom teaching mode is introduced into the online teaching platform for empirical research. According to the trial findings, more than 90.22 % of the students had improved their study habits. The network teaching platform proposed by the research institute can stimulate college students’ learning interest and improve their learning ability to a certain extent.

The world wide web acts as the dominant tool for data transmission due to access such as data retrieving and data transactions. The retrieval of data from the web is a complex procedure due to the large volume of web domain. The basic uses of the websites are described through web usage mining, which mines the weblog records to identify the pattern of accessing the web pages through the user. The web page prediction assists the web users in finding the plot and obtains the information as to their requirements. Several effective algorithms have been developed to mine association rules that make the predictive model more appropriate for web prediction. They can be commonly revised to ensure the changing feature of web access patterns. The Apriori algorithm involves extracting the recurrent itemset and interrelation rule that learns the relational data is commonly utilized for web page prediction. The Apriori algorithm remains the standard model for deriving the patterns and rules from the datasets in co-operative rule extraction. The Apriori algorithm thus generates large mines associated rules for web page prediction. Hence, to select the best rule, the proposed deer hunting rooster-based chicken swarm optimization algorithm is used by integrating the cockerel search agents’ dominating social search creatures’ hunting habits and their traits of looking for food. Further, the neural network (NN) is employed in this research for the prediction of web pages with minimum error. The trained NN is a technique of unsupervised learning that analyzes a dataset of input to produce the desired result, in which the effectiveness of the NN is enhanced by optimal tuning of weight by the adaptive deer hunting rooster-based chicken swarm optimization algorithm. The experimental analysis illustrates that the proposed adaptive deer hunting rooster-based chicken swarm optimization frameworks inherit lower error measures such as mean deviation = 139.89 and symmetric mean absolute percentage error$=$0.45579 for the FIFA dataset. The proposed web page prediction models’ L2 norm and infinity norm are 58.017 and 14, respectively, for the MSNBC_SPMF dataset.

Finding association rules is an important data-mining problem and can be derived based on mining large frequent candidate sets. In this paper, several graph models in finding association rules are proposed and an improved Apriori algorithm based on graph models is generated. A numerical experiment and comparison to the Apriori algorithm are carried out. Finally two interesting heuristic graph models are presented which may be useful in some special cases.

Due to continuous improvement of medical information construction, the scale of medical data is expanding, and its value will continue to increase. However, the traditional storage architecture cannot store such large amounts of medical data. Not only that, the common mining algorithm cannot analyze the value of potential information, largely hindering the development of medical wisdom. According to the concept and characteristics of smart medical, this paper proposes an architecture scheme of a medical cloud platform. Based on MapReduce, our Apriori medical data mining algorithm is conceived by introducing a degree of interest to improve the classic Apriori algorithm and combining with the knowledge of the cloud. The algorithm can ease the development bottleneck encountered by current smart medical. Overall, the proposed technology is more efficient and is able to apply itself more extensively in the field of medical research.

The association rule mining is an important topic in the data mining. Association rule mining aims to find rules in the transaction database with the minimum support and minimum confidence which are user given. Apriori algorithm is one of the most influential algorithms for mining Boolean association rules, but it is ineffective because of large candidates and ineffective calculation for support. In order to find all the frequent item sets from the transaction database efficiently and quickly, an improved Apriori algorithm is presented to solve the bottleneck problems of the traditional Apriori algorithm. First of all, frequent item format is necessary, aiming to reduce the rate of memory occupancy in the process of frequent item generation, and then convert the storage structure of transaction records. Finally, it divides frequent 1-item into different groups in order to implement parallel computing. The experiment results show the improved Apriori algorithm can improve the efficiency of the original algorithm effectively.