Big Data Mining Method of Marketing Management Based on Deep Trust Network Model

Through big data mining, enterprises can deeply understand the consumer preferences, behavior characteristics, market demand and other derived data of customers, so as to provide the basis for formulating accurate marketing strategies. Therefore, this paper proposes a marketing management big date mining method based on deep trust network model. This method first preprocesses the big data of marketing management, including data cleaning, data integration, data transformation and data reduction, and then establishes a big data mining model by using deep trust network to realize the research on the classification of marketing management data. Experimental results show that the proposed method has 99.08% accuracy, the capture rate reaches 88.11%, and the harmonic average between the accuracy and the recall rate is 89.27%, allowing for accurate marketing strategies.

Influence of Healthy Physical Fitness on the Teaching of Aerobics Course Based on Big Data Mining Technology

Healthy physical fitness is one of the hot topics discussed by scholars at home and abroad in recent years, and it is a key indicator for evaluating students’ physical function and body shape. Aerobics, also known as bodybuilding, means that the body and health of students should have a better promotion effect, but in reality, many students found that after elective aerobics, body shape and health level basically did not improve, which is related to the setting of aerobics courses, especially the lack of physical training. Aerobics and other sports have common requirements in physical training, such as strength quality, speed quality, endurance quality, agility quality, and flexibility quality. This article is aimed at studying the impact of healthy physical fitness based on big data mining technology on the teaching of aerobics. On the basis of analyzing the process of data mining, the composition of healthy physical fitness, and the role of aerobics, it is used to test students in a certain university through experimental methods and statistical methods. Carry out aerobics teaching experiment, and compare and analyze the data measured by the experimental samples. The experimental results show that the use of healthy physical fitness in aerobics teaching can effectively promote the learning and improvement of aerobics skills.

Dynamic Distributed and Parallel Machine Learning algorithms for big data mining processing

PurposeThis work can be used as a building block in other settings such as GPU, Map-Reduce, Spark or any other. Also, DDPML can be deployed on other distributed systems such as P2P networks, clusters, clouds computing or other technologies.Design/methodology/approachIn the age of Big Data, all companies want to benefit from large amounts of data. These data can help them understand their internal and external environment and anticipate associated phenomena, as the data turn into knowledge that can be used for prediction later. Thus, this knowledge becomes a great asset in companies' hands. This is precisely the objective of data mining. But with the production of a large amount of data and knowledge at a faster pace, the authors are now talking about Big Data mining. For this reason, the authors’ proposed works mainly aim at solving the problem of volume, veracity, validity and velocity when classifying Big Data using distributed and parallel processing techniques. So, the problem that the authors are raising in this work is how the authors can make machine learning algorithms work in a distributed and parallel way at the same time without losing the accuracy of classification results. To solve this problem, the authors propose a system called Dynamic Distributed and Parallel Machine Learning (DDPML) algorithms. To build it, the authors divided their work into two parts. In the first, the authors propose a distributed architecture that is controlled by Map-Reduce algorithm which in turn depends on random sampling technique. So, the distributed architecture that the authors designed is specially directed to handle big data processing that operates in a coherent and efficient manner with the sampling strategy proposed in this work. This architecture also helps the authors to actually verify the classification results obtained using the representative learning base (RLB). In the second part, the authors have extracted the representative learning base by sampling at two levels using the stratified random sampling method. This sampling method is also applied to extract the shared learning base (SLB) and the partial learning base for the first level (PLBL1) and the partial learning base for the second level (PLBL2). The experimental results show the efficiency of our solution that the authors provided without significant loss of the classification results. Thus, in practical terms, the system DDPML is generally dedicated to big data mining processing, and works effectively in distributed systems with a simple structure, such as client-server networks.FindingsThe authors got very satisfactory classification results.Originality/valueDDPML system is specially designed to smoothly handle big data mining classification.

Enterprise Financial Risk Management Using Information Fusion Technology and Big Data Mining

This paper aims to study enterprise Financial Risk Management (FRM) through Big Data Mining (BDM) and explore effective FRM solutions by introducing information fusion technology. Specifically, big data technology, Support Vector Machine (SVM), Logistic regression, and information fusion approaches are employedto study the enterprise financial risks in-depth.Among them, the selection offinancial risk indexes has a great impact on the monitoring results of the SVM-based FRM model; the Logistic regression-based FRM model can efficientlyclassify financial risks; theinformation fusion-based FRM model uses a fusion algorithm to fuse different information sources. The results show that the SVM-based and Logistic regression-based FRM models can manage and classify enterprise financial risks effectively in practice, with a classification accuracy of 90.22% and 90.88%, respectively; by comparison, the information fusion-based FRM modelbeats SVM-based and Logistic regression-based FRM models by presenting a classification accuracy as high as 95.18%. Therefore, it is concluded that the information fusion-based FRM is better than the SVM-based and Logistic regression-based models; it can integrate and calculate multiple enterprise financial risk data from different sources and obtain higher accuracy; besides, big data technology can provide important research methods for enterprise financial risk problems; SVM-based FRM model and Logistic regression-based FRM model can well classify enterprise financial risks, with relatively high accuracy.

Multimedia Computer-Aided Industrial System Design Based on the Study of Big Data Mining Algorithm

Aiming at the problems of the multimedia computer-aided industrial system, this paper puts forward the application of big data mining algorithm to multimedia computer-aided industrial system design and analyzes in detail the impact of multimedia technology on industrial quality. This paper introduces the advantages of using big data mining algorithm in multimedia computer technology course, shows the operating environment to be met by using the multimedia computer-aided industrial system, follows the guiding principles of the overall design learning theory and artistic conception cognition theory, supplements specific industrial examples, and discusses multimedia industrial design.