Optimization of Intelligent Data Mining Technology in Big Data Environment

In this paper a few case studies pertaining to breast cancer, mushroom, larynx cancer and other datasets are studied to find the utility of association rule mining using Weka tool. Three association algorithms - Apriori, PredictiveApriori and Tertius Algorithms are employed to discuss different case studies. A comparative study of the three algorithms is also made. Further architecture for implementing the association rules on datasets using Weka is also given. The analysis reveals that although the implementation of the three algorithms gives the strong association rules they have problems with the number of cycles taken to generate the frequent itemsets, minimum support needed, memory utilized and non-numeric data.

This work proposes an intelligent grey-wolf-optimizer-improved Apriori algorithm (GWO-Apriori) to mine the association rules of hidden dangers in hydrogen pipeline transmission stations. The optimal minimum support and minimum confidence are determined by GWO instead of the time-consuming trial approach. Experiments show that the average support and average confidence of association rules using GWO-Apriori increase by 29.8% and 21.3%, respectively, when compared with traditional Apriori. Overall, 59 ineffective association rules out of the total 105 rules are filtered by GWO, which dramatically improves data mining effectiveness. Moreover, 23 illogical association rules are excluded, and 12 new strong association rules ignored by the traditional Apriori are successfully mined. Compared with the inefficient and labor-intensive manual investigation, the intelligent GWO-Apriori algorithm dramatically improves pertinency and efficiency of hidden danger identification in hydrogen pipeline transmission stations.

Archives, as the materials for all kinds of work assessments, are the imprints of the past and the basis for guiding the future. Compared with traditional archive management methods, informatized archive management is more efficient and of better quality. The efficiency of various departments of society has made a qualitative leap and improvement under the background of the information age. To develop archive management business and make archive management more efficient and safe, it is necessary to explore a scientific and efficient informatized archive management work mode on the practical road of the new era. The advent of the big data era has brought fundamental changes to data mining technology. It can analyze and intelligently mine massive data, making it possible to mine data at a deeper level. Therefore, it is necessary to actively explore archive data mining technology, so that data mining technology can be widely applied to archive management work, as far as possible to mine and transmit potential archive information, play its due role, and maximize the economic and social benefits of archive data. This article, through the analysis of the characteristics of archive management under the big data environment, points out the problems existing in the current archive management work and the challenges and necessity of innovation in the archive management mode under the big data background, and discusses how to improve the innovation of the archive management work mode from practice

Objective Training monitoring is an important part of scientific training, and also accumulated a large amount of data, but the analysis and evaluation of biochemical indicators are mostly concentrated on the level of experience and the general, phased and individualized research application of statistical methods. The data mining technology is applied to the analysis and evaluation of the biochemical indexes of competitive sports, the analysis of the data is carried out in the deep level, the potential, new and useful information and knowledge are extracted, and the new exploration ideas are carried out for the analysis of the biochemical indexes of competitive sports, and a more reliable and more powerful data branch is provided for the scientific and efficient training support.
 Methods Using the literature data method, logic analysis method and expert interview method, the application of the current data mining technology in the analysis of biochemical indicators is summarized.
 Results The scientific analysis and evaluation of athletes' physical function status has been the focus of domestic and foreign coaches and sports researchers. The application of data mining technology in sports biochemical indicators is also becoming more and more extensive. For example, Mao Jie and others applied the gray ART clustering model analysis method to the monitoring of competitive sports biochemical indicators. Through this data mining model, the coach can easily judge the athlete's competitive physical condition, and can provide a scientific basis for correct training according to the different competitive conditions of each athlete, using different training guidance programs and training methods. Ma Jing et al. explored the feasibility of applying decision tree algorithm and association rules in volleyball biochemical analysis. It was found that C5.0 decision tree and Apriori association rule algorithm can be used to predict and analyze the technical level of women's volleyball players. Li Guangjun and others successfully applied the association rule data mining to the biochemical data analysis of canoeists, and provided a basis for scientific decision-making and analysis of sports training and athlete selection. Zhang Hui designed a data mining system for sports biochemical index based on association rules. The results show that the system has fast data mining rate, short time consuming and high reliability. It provides a more scientific evaluation standard for the data mining of sports biochemical index, and also provides a basis for the future training program.
 Conclusions With the development of competitive sports, in order to achieve new heights, the application of data mining technology to vast biochemical data is of great significance for the establishment of scientific training evaluation methods and standards, and is also the inevitable development of future sports scientific research.

As an efficient data analysis tool, data mining can discover the potential association and regularity of massive data, and it has been widely used and played an important role in business decision, medical research and so on. However, the data mining technology is also a double-edged sword, in bringing convenience at the same time, will also cause the user’s privacy leak problem. In order to solve the problem, the symmetric searchable encryption technology is introduced into the association rule mining system to protect the privacy, and privacy preserving mining system of s (PP-MSAR) in OpenStack-based Cloud environment is designed. In order to solve the problem that the existing data mining algorithms can’t deal with large-scale data, this paper uses the computational power of Hadoop platform and add the global pruning technique to the existing algorithm based on MapReduce association rules, so that the counting of frequent item sets get reduced. At the same time, this paper add frequent matrix storage method into the distributed association rules algorithm and realize he algorithm of mining association rules for frequent matrix storage based on MapReduce. In addition, the introduction of symmetric searchable encryption technology to support the cloud server-side ciphertext retrieval, on the one hand to ensure that users stored in the database information will not be leaked to the outside for others, on the other hand also to ensure that the user data for the system staff confidential. Finally, we test the system, and the results show that the system can carry out association rules mining under the premise of protecting user privacy, and provide the correlation degree between data, which has certain practical significance and application value.

Global darknet monitoring provides an effective way to observe cyber-attacks that are significantly threatening network security and management. In this paper, we present a study on characterization of cyberattacks in the big stream data collected in a large scale distributed darknet using association rule learning. The experiment shows that association rule learning in the darknet stream data can support strategic cyberattack countermeasure in the following ways. First, statistics computed from malware-specific rules can lead to better understanding of the global trend of cyberattacks in the Internet. Second, strong association rules can lead to further insights into the nature of the attacking tools and hence expedite the diagnosis. Then, the discovery of emerging new attacks may lead to early detection and prompt prevention of pandemic incidents, preventing damage to the IT infrastructure and extensive financial loss. Finally, exploring the knowledge in the frequent attacking patterns can enable accurate prediction of future attacks from analyzed hosts, which could improve the performance of honeypot systems to collect more pertinent malware information using limited system and network resources.

In recent years, database technology has improved significantly, and database management systems have gained widespread adoption. As a result, the volume of data saved across numerous databases has increased exponentially. However, the vast majority of information is hidden beneath this mountain of data. The goal of this study is to get a comprehensive understanding of the decision information system employed in the Internet of Things for intelligent manufacturing data processing. The proposed Decision support system (DSS) information processing is accomplished through the use of an IoT-based intelligent manufacturing data mining model. Numerous DM algorithms that are frequently encountered are analyzed, including the ARS and Apriori Algorithm (AA). The Decision Tree data mining algorithm is investigated, as is the generation of several Decision Trees and the pruning algorithm for digital twins. The findings demonstrate that data mining technology is capable of analyzing statistical data from a variety of angles and perspectives by modeling, classifying, and grouping large amounts of data as well as discovering correlations between them. Additionally, statistical work involves the calculation of data and the use of their correlations to aid in decision analysis. The proposed theoretical framework demonstrates how DSS-integrated components can work cooperatively in Intelligent Manufacturing to define a stable data flow within the Internet of Things. Particular emphasis is placed on conceptualizing the decision support system's integrated performance.

With the increasing investment in education in China, higher education institutions have higher requirements for the introduction of teachers. This research mainly discusses the development and training strategies of college teachers based on data mining technology. Data mining technology is dedicated to data analysis and understanding, and the technology of revealing the information contained in the data. It is a frontier research topic in the field of information and database technology. Therefore, the system of monitoring and evaluation of college teaching quality based on data mining is designed for the management of educational affairs in colleges provided convenience. This paper selects an unsupervised classification method: a cluster analysis. This method can not only obtain reasonable classification results but also give consideration to the comprehensiveness of employee development and give reasonable development suggestions for each employee through classification results. For a series of introduced teacher’s information, personnel management module should provide the following functions: teacher information management, contract information management, resignation information management, and query teacher personnel information. The recruitment management module often collects the candidate information, registers and stores it, and then conducts a series of personnel screening for these candidates according to the recruitment criteria, and finally determines the possible candidates. Afterwards, a series of comprehensive evaluations are carried out on these selected candidates, and finally the candidates are selected for admission based on the comprehensive performance of the candidates. The K-means clustering algorithm in the cluster analysis method is adopted. This algorithm has the excellent characteristics of high computational efficiency and is suitable for the operation of large amount of data. Through the clustering algorithm, a reasonable assessment method is established, and it is effectively used in the human resources assessment management system. Among the introduced teachers, the number of teachers whose professional title is high, the highest degree is doctorate, and the number of teachers whose papers are published at SCI level accounts for 16%. The data tested by the data mining tool contains 1,400 rows of data. The minimum support is 5%, and the minimum confidence is 90%. This study is helpful for the rational planning of human resources and the promotion of comprehensive competitiveness of colleges and universities.

Social alienation significantly impacts maintenance hemodialysis (MHD) patients. This study aimed to identify key factors associated with social alienation in MHD patients using association rule mining to inform interventions. A cross-sectional study enrolled 250 MHD patients from 11 centers (March-April 2025). Social alienation was assessed using the General Alienation Scale (GAS). Sociodemographic, clinical, and treatment variables were analyzed using the Apriori algorithm with thresholds: support ≥ 30%, confidence ≥ 50%, lift > 1. Among participants, 49.2% (n = 123) reported social alienation. Seven strong association rules were identified. Key combinations included: Urban employee insurance + thrice-weekly dialysis (support 58.8%, confidence 54.4%); Male + urban employee insurance + thrice-weekly dialysis (support 36.8%, confidence 54.4%); Age 36-50 + Han ethnicity (support 31.2%, confidence 56.4%). A dialysis treatment plan of 4-5h per week for three times, middle-aged working patients, and limited support were prominent risk factors. Social alienation in MHD patients is multifactorial. Interventions should include flexible dialysis scheduling, multidisciplinary psychosocial support, and community networks. Policy reforms addressing insurance-related employment stress are needed. Data-driven strategies enhance chronic disease management.

Construction of the Japanese interactive analysis system based on perception model and pattern mining is studied in this paper. In the multi-segment support model, the minimum support that meets the required conditions is set for each individual item in the data item set. When looking for frequent sets, use the maximum or minimum value of the minimum support of each item in the related item set to mine. Based on this model, the pattern mining framework is defined. The counting matrix improves the discovery efficiency of the positive and negative frequent items. In the strong association rule discovery stage, and the interactive model is defined. The proposed model is simulated on the real scenarios and the experiment is conducted.

Multi-objective economic load dispatch method based on data mining technology for large coal-fired power plants

In recent years, with the continuous development of the country’s Internet platforms, China has gradually entered the e-commerce era of national online shopping, and more and more e-commerce platforms and stores have adopted intelligent recommendation systems to increase transaction rates. However, it is not easy for consumers to filter out the products they want from a large amount of information. The emergence of intelligent recommendation systems provides great convenience for people to screen out personalized products that meet their own characteristics. However, the algorithms used in traditional recommendation technology focus on the single-computer environment and do not consider the performance of the recommendation method when distributed parallel processing is required in the big data environment, so it cannot meet the personalized needs of users in the big data environment. Aiming at the new requirements for the development of e-commerce intelligent recommendation technology in the big data environment, this paper uses the big data processing technology based on cloud computing and focuses on the realization technology of the e-commerce intelligent recommendation algorithm and the comprehensive evaluation method of the recommendation system in the big data environment. A prototype system of personalized intelligent recommendation based on cloud computing has been developed, which is of great importance to meet the needs of e-commerce personalized intelligent recommendation in the big data environment, improve the effectiveness, scale, and real-time performance of the personalized intelligent recommendation system, and improve the level of personalized precision marketing., which is of theoretical significance and economic value.

Due to the increase of retail sales in Egypt and all over the world, came the importance for the managers of supermarkets to develop marketing strategy to maximize their profits, by getting rid of inactive products. Product bundling is one of the most important marketing strategies used to get rid of stock by making integrated bundles of inactive products and demanded products with discount prices. We can do that through our recommendation system and also increase customers' faith by keeping up with their purchase habits changes in low prices. In this study, first association rules are applied to find the best-integrated bundles with optimal suggested bundle size according to customer habits. Second testing these resulting product bundles to eliminate bundles didn't contain Products aim to get rid of them. Finally the given suggested bundles’ elements replaced with stagnant products that are the same kind of product in the bundle but with another trade. During that study algorithms (Apriori and FP-growth) were studied. Although the two algorithms give strong association rules, and the results were so near. But FP-Growth algorithm was more efficiency as Apriori algorithm caused problems with minimum support parameter as, in a small system transaction, big minimum support did not work using it. Also, it makes the memory PC faces memory hangs when minimum support was very low. On the other hand, all of this didn't appear with FP-Growth algorithm, and it was faster in dealing data.

Along with the development of artificial intelligent and computer science, data mining has become a powerful analytical tool to obtain the interested and important information from the amount of noisy and fuzzy data. In this paper, data mining technology is applied to the charging system in hospital. Usually, many unnecessary medical requirements are added to the treatment of patients, which could result in the extra pay and time for such treatment. Thus, this paper presents the definition of one-fee system in hospital for the common disease cases by applying data mining technology, in which, artificial neural networks and association rules (e.g., apriori algorithm) are presented. The presented one-fee definition might provide a reference for the charging system in hospital.

This paper focuses on the “Internet + education” model and describes the feasible strategy of using data mining technology to establish a teaching quality monitoring mechanism in colleges and universities. Through the study of teaching quality monitoring pathways, such as teachers’ teaching level, students’ academic status, and course learning effectiveness, this paper’s main content provides a directional guide. Based on the teaching quality monitoring pathway, we choose the related algorithms of data mining technology, such as cluster analysis, association rule, and factor analysis, to be applied to the various aspects of the teaching quality monitoring pathway. The clustering algorithm is used to classify 1500 teachers in a university into 4 categories, such as excellent teaching effect, good teaching effect, etc., as well as to cluster students into 6 categories, such as excellent in all the courses and low to medium level, according to the course grades. In addition, this paper analyzes students’ course learning effectiveness, academic level, and career guidance using association rule algorithms, factor algorithms, and decision tree algorithms, respectively. From teachers’ teaching to students’ learning and employment, this paper constructs an all-round teaching quality monitoring mechanism using data mining technology, which contributes to the improvement of teaching quality in colleges and universities.