Application Of Data Mining Technology Research Articles

Research on Anomaly Detection of Ship Behavior Based on Data Mining

With the increase in the number of ships and the intensifying maritime traffic, maritime accidents such as ship collisions and grounding occur frequently, posing serious threats to people's lives and property. Therefore, timely detection and correction of abnormal ship behaviors are of great significance for improving navigation safety. The Automatic Identification System (AIS) can provide real-time information on ships' positions, speeds, headings, and more, offering a rich data resource for data mining. Through the analysis of these data, it is possible to deeply explore the navigation patterns and characteristics of abnormal behaviors of ships. This paper applies data mining technology to the detection of abnormal ship behaviors, which not only improves the accuracy and efficiency of detection but also provides maritime regulatory authorities with richer decision-making support.

Apriori Algorithm to Predict Availability of Beauty Products

This study introduces the Apriori algorithm in beauty product availability prediction system as a solution to enhance stock prediction accuracy and mitigate inventory risks in the beauty industry. By applying data mining technology, specifically the Apriori algorithm, Kazana Kosmetik aims to gain insights into consumer purchasing patterns to optimize operations. The research analyzes transaction data to identify key buying patterns and improve stock management strategies. The results reveal seven main purchasing patterns with an average confidence value of 0.414, offering valuable guidance for Kazana Kosmetik in inventory control and marketing tactics. By leveraging data mining techniques, companies like Kazana Kosmetik can streamline sales strategies and enhance customer satisfaction. This research underscores the effectiveness of the Apriori algorithm in predicting beauty product availability and its potential to revolutionize operational efficiency in the cosmetics market.

The use of intelligent real-time image acquisition system using data mining technology in news acquisition and editing practice

Aiming to address the timely dissemination of news information, this work explores the clever utilization of data mining (DM) technology and deep learning (DL) algorithms to construct an intelligent real-time news image acquisition system to meet the urgency of news dissemination needs. First, this work introduces an intelligent real-time news image acquisition system and provides a detailed analysis of its principles and advantages. Throughout this process, the crucial role of DM technology in news image classification and automation is emphasized, especially in dealing with rapidly evolving news events. Next, the work establishes an intelligent real-time news image acquisition model based on DL algorithms, which integrates the essence of DM technology. Through this fusion, the research objective is to enhance the performance of the news image acquisition system to achieve higher real-time and accuracy, which is vital for the swift delivery of news information. Finally, this work investigates the application of the intelligent news image acquisition system in network communication to ensure its adaptability to various network communication scenarios while maintaining accuracy and real-time capabilities. The research results demonstrate that the application of DM technology in combination with DL algorithms can effectively meet the practical needs of the news industry, enhancing the automation of news image processing and enabling faster information delivery to the audience. Notably, the AlexNet model employed performs exceptionally well, achieving recognition rates of up to 99.6% after data augmentation or equalization processing, with an accuracy of 90.9% and a high specificity of 93.38%. This indicates outstanding overall classification accuracy and negative class accuracy, even when distinguishing between news and non-news scenarios. These results clearly underscore the connection between DM technology and news acquisition and editing practices, and emphasize its potential to improve the efficiency and accuracy of real-time information dissemination. The research’s contribution and innovation lie in the fusion of DM technology with DL algorithms to build an intelligent real-time news image acquisition system. This fusion enhances the automation and classification performance of news images, thereby improving the real-time and accuracy of news information. Furthermore, the work strongly emphasizes improving the real-time and accuracy of the news image acquisition system to ensure the swift delivery of information, which is of utmost importance in rapidly changing news events.

Application of data mining technology in analysis of influencing factors of Smart electric power emergency rescue

Because electric emergency rescue is affected by many factors, there are certain problems in the rescue process. In order to improve the power emergency rescue effect, based on data mining technology, this paper selects the neural network algorithm as the main analysis algorithm of the model, and uses the multi-layer forward neural network topology to perform modeling and correlation analysis. Moreover, this paper establishes the functional architecture of the power system emergency plan deduction system based on the analysis of the demand for the power system emergency plan deduction system. In addition, this paper combines the power emergency rescue system requirements to build the model function module, proposes a mobile model based on the emergency rescue scene, and analyzes its realization process. Finally, in order to study the system performance, this paper designs experiments to analyze the model performance, and combines mathematical statistics to draw charts and score the system model. The research results show that the power rescue influence factor analysis model constructed in this paper is effective.

An overview of the application of data mining in the campus

With the advent of campus informatization, it has been observed that the utilization of big data has the potential to facilitate the integration of education and enhance students' learning efficiency. The utilization of big data technology has been increasingly prevalent in contemporary society, leading to a growing reliance on its applications by the general population. Consequently, there is a rising need for educational platforms that can cater to this evolving demand. In the present scenario, China is persistently augmenting the development of campus infrastructure with the aim of attaining modern education objectives and ushering in a new era of educational facilities. The utilization of big data technology, its current status in contemporary usage, and the strategies for achieving widespread adoption have emerged as crucial concerns within the present wave of information technology throughout educational institutions. Simultaneously, the utilization of data mining technology enhances the learning environment for students, mitigates the constraints of campus intelligence, and offers substantial assistance for many educational endeavours inside the campus setting. The application of data mining technology for the advancement of campus management has emerged as a pivotal focal point and a significant breakthrough. This research examines the concept of the convenience zone in data mining and explores its application policy by conducting a comprehensive assessment of relevant literature. The study reveals that there is a lack of widespread awareness and adoption of big data in various contexts where it is necessary. Additionally, the potential utilization of big data at educational institutions, namely on campus, is explored.

Research on the application of big data mining technology based on neural network

In the development of social construction, all walks of life have the application of data mining technology, with the continuous development of science and technology, data mining technology can not only use the hidden law to solve the problem of time, but also can grasp more valuable data in the increasingly competitive social environment. Originally, after understanding the application research status of big data mining technology, based on the application of big data mining technology with neural network as the core, empirical analysis was carried out with Guanhealth big data intelligent analysis system as an example, in order to verify the unique value of data mining technology.

Exploring the Communication Path of Civic Education in Cyberspace Using Data Mining Technology

Abstract A recommendation algorithm is a specific application of data mining technology, which is an important means to expand the dissemination of Civics education in cyberspace. The elbow method is employed in this paper to determine the best number of clusters to cluster the Civics course evaluation dataset and finish preprocessing large-scale data. Aiming at the problem of different importance of rating bias between items, the traditional Slope One algorithm is optimized by comprehensively considering the relationship between the number of common ratings and the similarity between items and the prediction results. The optimized Slope One algorithm is used to perform one round of prediction and fill the scoring matrix to solve the data sparsity problem and improve prediction accuracy and stability. The optimized algorithm reduces by 8.56% compared to the user-based collaborative filtering algorithm, 5.83% compared to the item-based collaborative filtering algorithm, and 5.25% compared to the content-based recommendation algorithm, with a significant improvement in accuracy, recall, and F1 value. The four hypothesis validations indicate that the diversity, personalization, and accuracy of resource recommendations all have positive effects on the dissemination effect of civic education. The recommendation algorithm’s dissemination effect score in this paper is 3.781, which is significantly superior to the dissemination effect of Civic Education.

Innovation of Operation Mode of Traditional Enterprises in the Era of Digital Economy--Taking Traditional Cultural Enterprises as an Example

Abstract The essence of a cultural enterprise’s digital transformation is the process by which it metamorphoses into a new type of cultural enterprise by using new digital technologies to improve its core business and product concepts. This paper delves into the need for digital transformation in traditional cultural enterprises and devises a strategy for this transformation. It applies data mining technology to customer relationship management, analyzing customer value using the K-means algorithm and weighted association rules. The CRM system, built using data mining technology, can categorize customers into four types and implement corresponding marketing strategies for customers with different values. Using Company A as an example, we verify the impact of this innovative model on revenue, profit, and operational capacity. After the digital transformation of Company A, the operating profit maintains a fast growth rate, in which the difference in profit between December and November reaches 31,132 yuan, and the gap between the company’s inventory turnover rate and that of the same industry in December is only 0.1%. The digital transformation of traditional cultural enterprises has facilitated the company’s rapid growth.

Research on the Reform and Practice of Informatization Mode of Adult Higher and Continuing Education Academic Records Management in the Context of Three-Whole Parenting

Abstract Information technology reform is an important way for schools to realize efficient management and information technology should be introduced into the management of student registration files to accelerate the process of informationization of registration file management. This paper builds an informatization database of adult higher continuing education student registration file management based on informatization technology and carries out the design of the main business process of student registration files. The multi-layer stacked LSTM network in the ELMo model is utilized to learn the preprocessing of the text of the school records data, and the CART decision tree algorithm is improved based on the reinforcement learning mechanism in order to improve the classification prediction accuracy of the decision tree in the dataset. In order to verify the feasibility of the school registration file informationization database constructed in this paper, a test analysis is conducted for the application of data mining technology and informationization database. The results show that the check accuracy of the CART decision tree algorithm improved by the reinforcement learning mechanism is around 80%, and the throughput of this paper’s database reaches 4827ops/s when the data writing volume is 1*105GB. When the number of threads reaches 128, the throughput of this paper’s informative database is around 5486ops/s, and when the data volume reaches 12*105 entries, the database in this paper the query response time is around 602ms. Under the background of three-round education, it is necessary to fully integrate information technology with the management of school registration files, which will lead to the innovation of the informationization mode of the management of school registration files in adult higher education.

A hybrid data-driven approach for the analysis of hydrodynamic lubrication

The application of data mining technology has intensively advanced tribology research. While recent lubrication studies have highlighted the importance of data mining, researchers have not fully bridged the gap between massive lubrication data and intrinsic lubrication mechanisms. Thus, by revisiting lubrication modelling from the data-driven and physics-informed perspectives, we aim to construct a hybrid approach for hydrodynamic lubrication classification and prediction, where data-driven methods are combined with physics-informed approaches to achieve a fast and accurate prediction of the hydrodynamic lubrication scenario. Our approach will spur the application of data mining methods in lubrication studies.

Research on the ideologic and politic education of college students based on big data analysis

Abstract This paper firstly researches the ideological and political education in higher education institutions in the background of the big data era and puts forward the innovation strategy of ideological and political education mode in higher education institutions in the big data era. Secondly, it studies the application of data mining technology in colleges and universities for ideological and political education. Based on data mining technology, the data on ideological and political education is determined by the clustering algorithm, followed by a quantitative examination of the policy text of ideological and political education of colleges and universities by calculating the quantitative statistical analysis of policy text, word frequency, and word cloud analysis. Then, the research subjects were selected, and the initial data were obtained using questionnaires, further pre-processing, and further empirical analysis of ideological and political education based on data mining technology. The consequences show that the proportions contained in the three types of score bands, namely 17.99%, 59.38% and 9.96%, basically match with the sample proportions accounted for by the three types of grades assessed by clustering, namely 30.51%, 62.02% and 8.47%, which confirms that such a model of data mining adopted according to the quantitative table of ideologic and politic education assessment in higher education institution is a very successful model. This study contributes to the innovation of online ideological and political education work in higher education institutions in the context of big data.

Research on the application of data mining technology in music therapy in the age of information technology

Abstract The wide application of digital mining technology will certainly promote the development of music therapy. In this paper, we first analyze the development of music therapy and clarify the classification of orchestration. Secondly, for the time period model of mining data technology, the mathematical method of the ARMA model and the ARIMA model is investigated. Finally, the music therapy effect of guzheng performance training is predicted based on the ARIMA time series. In accordance with the playing sloped of guzheng clips from Gaojia opera, the relationship between the total number of subjects and the amount of music therapy sessions was examined, as well as the correlation among the number of active behaviors, self-expression, positive emotional expressions, and negative feelings of the subject children. The attentional concentration time showed a highly significant correlation with the number of times under music therapy, i.e., P<0.02, while the number of active behaviors and self-expression showed a significant correlation, i.e., P<0.04.

Application of data mining technology based on improved neural network algorithm in sports performance prediction

Application of data mining technology based on improved neural network algorithm in sports performance prediction

Application of data mining technology and intelligent information technology in the construction and management of the water conservancy project in Area A

Abstract At present, the construction and management efficiency of water conservancy projects is low, hydrological prediction errors are common, and the utilization rate of water resources is low. Based on this, this paper applies data mining technology and intelligent information technology to water conservancy project management. This will help to better study the construction and management of hydraulic projects. By adopting data mining technology, valuable data in hydraulic engineering can be extracted and analyzed. Through careful analysis and evaluation of the data, we can predict the runoff of reservoir hydrology in Area A. The experimental results show that the data mining technology can be used to predict the runoff of Reservoir A from January to December 2020. In addition, using machine learning techniques to make predictions, the prediction error rate varies by 6.2%. The research shows that the use of data mining technology can improve the efficiency of water conservancy project construction and management, and improve the utilization rate of the project.

Methodology of formation of fuzzy associative rules with weighted attributes from SIEM database for detection of cyber incidents in special information and communication systems

The article presents the method of forming associative rules from the database of the SIEM system for detecting cyber incidents, which is based on the theory of fuzzy sets and methods of data mining. On the basis of the conducted analysis, a conclusion was made about the expediency of detecting cyber incidents in special information and communication systems (SICS) by applying rule-oriented methods. The necessity of applying data mining technologies, in particular, methods of forming associative rules to supplement the knowledge base (KB) of the SIEM system with the aim of improving its characteristics in the process of detecting cyber incidents, is substantiated. For the effective application of cyber incident detection models built on the basis of the theory of fuzzy sets, the use of fuzzy associative rule search methods is proposed, which allow processing heterogeneous data about cyber incidents and are transparent for perception. The mathematical apparatus for forming fuzzy associative rules is considered and examples of its application are given. In order to increase the effectiveness of the methods of searching for fuzzy associative rules from the database of the SIEM it is proposed to use weighting coefficients of attributes that characterize the degree of manifestation of their importance in the fuzzy rule. A formal formulation of the problem of forming fuzzy associative rules with weighted attributes and which are used for the identification of cyber incidents is given. A scheme of their formation and application for identification of cyber incidents is proposed. The method of forming fuzzy associative rules with weighted attributes from the database of the SIEM is given. The problem of determining the weighting coefficients of the relative importance of SIEM system DB attributes is formulated and a method for its solution is proposed. The formulation of the problem of finding sets of elements that have a weighted fuzzy support of at least the given one and are used to form fuzzy associative rules with weighted attributes is given. Methods for its solution are proposed.

Using data mining technology to explore causes of inaccurate reliability data and suggestions for maintenance management

Reliability data reflects equipment safety and provides a reference for setting inspection period, thereby serving as crucial information for the implementation of equipment integrity management policies. The calculation foundation of reliability data is maintenance records of adequate data quality. However, maintenance records of doubtful quality are common. Despite excluding poor quality recodes and using only the remaining maintenance recodes to calculate the reliability data, the calculated results generally lack a sufficient degree of confidence. This study applied data mining technology, including quality metrics, the association rule, and clustering, to explore the cause of low-quality maintenance data. The results revealed that the low data quality of maintenance records was due to ineffective maintenance policies, the low integrity of key system columns, nonadherence to the policy, and misunderstanding of column definitions. The proposed method successfully identified the causes of low-quality maintenance records. By incorporating the method into the function module of a CMMS, operators can equip the system with self-diagnosis, self-supervision, and continuous optimization functions.

Effectiveness Analysis of Stock KDJ Indicator Method based on K-means Clustering

The application of data mining technology expands various techniques in stock investment. Among them, cluster analysis is one of the common means to study stock technical indicators. There is a problem in the current cluster analysis of stock technical indicators -- the lack of validation of large-scale stock technical indicators data sets. Most of them are suitable for the comprehensive analysis of technical indicators of a single stock or multiple stocks. Aiming at this problem, this paper takes "the validity analysis of the large-scale stock KDJ index set method based on K-means clustering" as the theme. Firstly, the k-means clustering algorithm was used to construct a deep analysis model (KDJ-k-means) for the KDJ index set of the Shenzhen Index component data group. Secondly, the K, D and J index sets of 2697 constituent stocks of Shenzhen Composite Index are analyzed experimentally. Finally, the results of integrated data mining are obtained. The KDJ-k-means model is an optimization scheme based on the KDJ index set using clustering technology, which provides an intuitive and efficient visual application for deep analysis of large-scale stock data groups.

A Survey of Fuzzy Pattern Tree Classification Algorithms

Data classification algorithm is the core content of big data mining. Its main function is to extract valuable knowledge and information,analyze the characteristics of all kinds of information, and provide data basis for further research. With the wide application of data mining technology, data classification algorithms continue to emerge and gradually improve. The classic classification algorithms include decision tree classification algorithm, naive Bayes algorithm, support vector machine classification algorithm, artificial neural network classification algorithm, fuzzy pattern tree (FPT) and so on. This paper summarizes several common algorithms in data classification algorithms, and analyzes their characteristics to understand their algorithm principles and application scenarios.

The Moderation Role of AI-Enabled Service Quality on the Attitude Toward Fitness Apps

Artificial intelligent technology is rapidly transforming the fitness apps landscape by applying data mining technologies within given parameters. These wide-ranging AI-enabled services improve user interface and enhance customers' experience when exercising with the fitness apps. The current study integrated the four antecedents—technological functions, intrinsic information quality, perceived enjoyment, and social connection—to investigate the moderating influence of AI-enabled service quality on users' attitude toward physical activity. PLS-SEM was used to analyze and validate a sample of 170 participants. The findings posited that individuals' attitude toward physical activity is encouraged by the (1) technological functions (2) intrinsic information quality, and (3) perceived enjoyment. Further, the moderating role of AI-enabled service positively influencing the attitude toward physical activity with technological functions was also established.

Application Research of Data Mining in MES Quality Management

With the increasing market competition, enterprises have continuously raised the requirements for product quality in order to gain a favorable position in the market competition. In industrial production, MES quality information management mainly involves the collection, statistical analysis, and utilization of information data that affect quality in daily production. In actual industrial production, the formation of product quality is the result of the interaction of numerous factors. Due to the many factors and scenarios involved, traditional data statistical analysis methods cannot accurately and effectively analyze the collected relevant data, and fail to fully mine the value of data. This paper proposes the application of data mining technology to MES quality information management systems and elaborates on the use of the K-means algorithm and Apriori association rule algorithm to analyze the related processing rules of parts in the production and processing process. The algorithm model is used to analyze the actual production and processing data of a certain enterprise, and finally, the value and application of parts association processing rules in actual enterprise production management are summarized.