Big Data Mining Algorithm Research Articles

Strategic Transformation of E-Commerce Big Data Classification and Mining Algorithms Based on Artificial Intelligence Era

Strategic Transformation of E-Commerce Big Data Classification and Mining Algorithms Based on Artificial Intelligence Era

Application Big Data and Intelligent Optimization Algorithms on Teaching Evaluation Method for Higher Vocational Institutions

INTRODUCTION: The optimization of the teaching evaluation system, as an essential part of teaching reform in higher vocational colleges and universities, is conducive to the development of higher vocational colleges and universities' disciplines, making the existing teaching more standardized.OBJECTIVES: Aiming at the problems of inefficiency, incomplete index system, and low assessment accuracy in evaluation methods of higher vocational colleges and universities.METHODS: Proposes a teaching evaluation method for higher vocational colleges and universities with a big data mining algorithm and an intelligent optimization algorithm. Firstly, the teaching evaluation index system of higher vocational colleges and universities is downgraded and analyzed by using principal component analysis; then, the random forest hyperparameters are optimized by the grey wolf optimization algorithm, and the teaching evaluation model of higher vocational colleges and universities is constructed; finally, the validity and stability of the proposed method is verified by simulation experimental analysis.RESULTS: The results show that the proposed method improves the accuracy of the evaluation model.CONCLUSION: Solves the problems of low evaluation accuracy, incomplete system, and low efficiency of teaching evaluation methods in higher vocational colleges.

Retracted: Analysis of the Development Trend and Scheme of Agricultural Electrification Intelligence Based on Big Data Mining and OLAP Tool Analysis Algorithm

Retracted: Analysis of the Development Trend and Scheme of Agricultural Electrification Intelligence Based on Big Data Mining and OLAP Tool Analysis Algorithm

Design of Customized Teaching Strategies Based on User Behavior Analysis in the Digital Transformation of Health Education

Abstract With the greater popularity of the Internet, on the one hand, online education platforms have flourished and education informatization has entered a new era, with massive learning behavior data generated by learners in different learning platforms. This paper constructs a portrait of student learning behavior based on the characteristics of their learning behavior in the health education class. Based on the clustering analysis method in the big data mining algorithm, the user’s behavioral characteristics are analyzed. On this basis, the algorithm is improved by using collaborative filtering personalized recommendation and introducing the LDA model, and a customized teaching model is constructed in the context of health education, and its application effect is explored. From the four dimensions of “course completion characteristics”, “teaching interaction characteristics”, “learning input characteristics,” and “learning achievement characteristics”, we analyzed different groups of students and investigated the effect of its application. “The behavioral characteristics of different groups of students were analyzed, and it was concluded that Group A and Group B performed better, but Group C accounted for a higher percentage of 24%. Finally, according to the collaborative filtering-based digital customized teaching on the students’ health education test scores for the pre and post-test, it was concluded that the average scores of the experimental subjects increased after the post-test. The mean scores for the third post-test were 4.27, 4.44, and 4.35, respectively. It can be concluded that the customized teaching model has a significant improvement in the students’ classroom teaching effectiveness.

A study on the influence of fashion advertising style on consumer perception based on big data technology

Abstract This paper establishes a dataset through big data mining algorithms, obtains data objects in the subspace according to data outlier characteristics, and derives the subspace outlier probability formula. This paper establishes a dataset through big data mining algorithms, obtains data objects in the subspace according to data outlier characteristics, and derives the subspace outlier probability formula. The data distribution characteristics are analyzed using entropy detection, and the multi-information entropy data are extracted for clustering detection by inputting the number of database samples for differential evolution. The adaptive search method is used for feature extraction of big data information flow, and text data with similar characteristics are divided into uniform fuzzy clustering centers to mine the optimal clustering indicators. Four categories of fashion photography styles were obtained by clustering fashion advertisement photos through data mining. Big data technology positively impacted the influence of consumers' consumption concepts, as their social self-concept increased from 4 to 10 points.

Correlation analysis between the quality of physical activity and public health based on artificial intelligence technology

Abstract In the context of artificial intelligence technology, the declining physical fitness of Chinese college students is still a serious social problem, and it is urgent to improve the physical health of college students. In this paper, the K-menns algorithm and decision tree CART algorithm are selected to be applied to the analysis and evaluation of college students’ physical health data based on the actual range of attributes of college students’ physical health data, the specific ideas of big data mining algorithm, and the characteristics and application fields of the algorithm are fully considered. Then, in the analysis of the physical health test data of class students, the percentage of the specific index amount to the total index amount is calculated, and the superior and inferior indexes are derived so as to realize the diagnosis of physical health posture. In order to investigate the correlation between physical exercise behavior and the health level of college teachers, a correlation analysis was conducted between the scores of 5 dimensions of physical exercise quality of college teachers and the scores of 8 dimensions of health level. The results showed that among the 5 dimensions, except for the behavioral intention and behavioral control dimensions, which did not have a statistically significant correlation with the physiological function dimension of health level, each of the remaining dimensions had a highly significant positive correlation with each dimension of health level P<0.01. This study promotes the modernization of physical fitness assessment of college students, which is important for maintaining health.

Intelligent analysis system of college students' employment and entrepreneurship situation: Big data and artificial intelligence-driven approach

This study proposes an analysis plan for college students' employment and entrepreneurship based on big data and artificial intelligence, aiming to provide an important basis for the development of employment guidance activities. To realize feature analysis and useful information extraction of large-scale text information data, this study adopts a big data mining algorithm based on fuzzy hierarchical clustering analysis and semantic similarity correlation feature extraction. The algorithm uses generalization mapping to construct a semantic concept tree, and a fuzzy analytic hierarchy process to judge the semantic similarity and relevance of big data. This study demonstrates the efficacy of a big data mining algorithm through experimental verification. Two public datasets were used to validate the proposed method. The experimental results revealed that the method could efficiently process and analyze large textual datasets, leading to superior data classification performance.

Analysis of the practical education model and its value in the context of big data in university civic education

Abstract In order to verify the feasibility of data mining for the practical education model and its value analysis of college civic education in the context of big data. Based on the big data mining algorithm-parallel density clustering algorithm, this paper designs five types of teaching evaluation indexes of Civic Education as experiments to explore the new direction of Civic Education practice in colleges and universities in terms of value perception and target, teaching content, and method, teaching interaction, and organization, learning input and support, and learning effectiveness and evaluation. The experimental indexes show that: through the big data mining algorithm, we can understand that the current students have relatively good feelings towards “Civic Education” and “Introduction” courses, with mean values of 4.03 and 4.01, respectively; they prefer current issues in teaching contents and methods, with mean values of 7.48; they prefer current issues in teaching contents and methods. The mean value is 7.48; the mean value is 5.45; the mean value is 5.12; the mean value is 5.12; the mean value is 6.85; the mean value is 6.85; the mean value is 6.85. The results also show that the big data parallel density clustering algorithm can provide the relevant data mining analysis for the college Civic Education practice. The results also show that the big data parallel density clustering algorithm can provide relevant mining data analysis for college thinking and political education practice and can present the innovative reform direction of college thinking and political education through data more intuitively. It also makes the college thinking education truly enjoy the dividends of the big data era.

Smart Pension System Based on Big Data Mining Algorithm

With the increasingly prominent problem of aging in our country, it has become an urgent task for our society to establish and improve a good old-age care system. At present, a new type of community smart elderly care model is gradually emerging, and has been actively explored and improved. In order to meet the needs of community elderly care services, big data mining algorithms are used to process data, and the smart elderly care system is connected with mobile APP and computer server equipment. Open source WebGIS technologies such as OpenLayers/Cesium, PostgreSQL/PostGIS, GeoServer, etc. are used as core supports, combined with front-end And back-end development technologies such as Vue and SpringBoot, etc., developed a mobile smart pension app and a web smart pension system based on open source WebGIS. This system can be more in line with my country's current smart elderly care service system, and accelerate the construction process of my country's intelligent elderly care.

Research on Artificial Intelligence Applications Based on Data Mining Algorithms in the Era of Big Data

At present, data has penetrated into every industry field and has become an important production factor. The scale of data is also expanding at an alarming rate. Big data is becoming the most significant label of this era. The concept of big data has subverted our understanding of traditional data, and has also caused technological changes in data acquisition, storage, analysis, mining, and visualization. The development of big data and related technologies is changing the important foundation of human production and lifestyles. Based on the understanding of the connotation of big data and data mining algorithms, this paper discusses the application of artificial intelligence based on data mining algorithms on its technical system and analyzes its future development trends, hoping to provide some reference for related research.

Application and Analysis of Big Data Mining in the Foreign Affairs Translation System.

Based on the expansion of scientific and technological capabilities, the trend of global integration has further strengthened, and the relations between countries have become closer and closer. Therefore, the foreign affairs translation system plays a very important role. Many scientific and technological projects have carried out research and analysis around the foreign affairs translation system. Nowadays, the wide variety of data and the complexity of languages in various countries force the processing structure of the foreign affairs translation system to be changed to adapt to the development of big data. In this context, this article studies the foreign affairs translation system based on big data mining technology and designs the application of a new foreign affairs translation system model. The results of the experiment are as follows: (1) The development status of big data mining technology and the problems existing in the current foreign affairs translation system are analyzed, and the research direction of the experiment is determined. The foreign affairs translation system is analyzed according to big data mining technology, which determines the technical guarantee for the research of this article. (2) In keeping with the traditional effective foreign affairs translation system, this article uses big data mining algorithm analysis, the fuzzy c-means clustering algorithm, and the BP neural network algorithm to identify and analyze the problems of the foreign affairs translation model about data analysis ability technology, which quickly and accurately analyzes the problems of the system and optimizes and improves according to the specific problems.

The product marketing model of the economic zone by the sensor big data mining algorithm

Since the reform and opening up, China's market economy has gradually prospered, and the development of various industries and their products has become a key research issue of the government. In the context of big data, this article believes that future industrial development should be driven by big data. At the same time, the advent of the era of big data will surely bring about changes in the marketing model of products in the economic zone. This article puts forward the research on product marketing model based on big data mining algorithm, the purpose is to do a detailed investigation of the core algorithm of data mining, in order to improve the marketing efficiency of enterprises and reduce the investment and loss of marketing. The method of this paper is to study the Hadoop parallel framework and the K-Means algorithm in the mining algorithm to collaboratively filter the big data mining algorithm, and integrate these algorithms into the marketing process. These methods can recommend products to target users based on their previous interests and other users who have similar interests. Finally, this article sorts the web pages according to the PageRank value of each node in order to achieve the purpose of accurately recommending products to customers. Through the K-means algorithm mining data experiment and financial product marketing examples, this article puts forward some suggestions and measures for the marketing model. The experimental results show that the customer response rate of precision marketing is 11.92 % higher than that of ordinary marketing, and the sales efficiency of marketers has improved significantly. In addition, precision marketing is better than ordinary marketing in many aspects such as the proportion of large orders, average consumption price, and refund rate.

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.

Analysis of the Development Trend and Scheme of Agricultural Electrification Intelligence Based on Big Data Mining and OLAP Tool Analysis Algorithm

To transform from a large agricultural country to a strong agricultural country, China must take the road of modernized, automated, and information-based agriculture. With the advantages of high efficiency, low cost, flexibility, stability, and reliability, electrified production is the material and technical basis for agricultural mechanization, automation, industrialization, and management modernization. Big data mining and OLAP tool analysis algorithm has powerful data processing ability and occupies a very important part in various fields. This article analyzes the current situation of agricultural electrification development, electrification development dynamics, and electrification application scope based on big data mining and OLAP tool analysis algorithm, and finally comes up with several suggestions for agricultural electrification development.

Application of intelligent teaching resource organisation model and construction of performance evaluation model

Abstract In the construction of intelligent teaching resources, a large number of resources are collected to meet the teaching needs, and its organisational structure, resource content and teaching quality are not fully recognised. At present, there are few studies on the performance evaluation of intelligent teaching, and the research angles are also different. Based on this, this paper constructs the application performance evaluation model of intelligent teaching resource organisation mode. Based on the simple analysis of intelligent teaching research and the development of big data mining algorithm, this paper uses data mining algorithm to realise resource mining, uses ant colony algorithm and association rules to mine information, carries out student evaluation on this basis, and uses ID3 algorithm to construct decision tree to analyse the main factors affecting teaching. Then the performance evaluation model is built, and the concept of fuzzy mathematics is introduced to realise the comprehensive evaluation based on analytic hierarchy process. Finally, simulation tests and experiments are used to analyse the effectiveness of the algorithm and the application of the model. The results show that the data mining algorithm based on association rules can mine more information and shorten the running time. The performance evaluation model based on fuzzy comprehensive evaluation can be applied to the evaluation of intelligent teaching, find the shortcomings in teaching, and confirm the effectiveness of the model.

Characteristics Analysis of Applied Mathematics in Colleges and Universities Based on Big Data Mining Algorithm Model

To analyze the mathematical nature of applied mathematics in colleges and universities, a method based on a model of big data mining algorithms is proposed. Firstly, the modeling is carried out through the deployment of nodes, which can accurately collect the characteristics of data information in the case of massive big data; secondly, the acquisition algorithm of multi-feature fusion is systematically optimized, which can avoid data interference and collect features quickly and accurately; thirdly, by transforming the multidimensional application-oriented university applied mathematics discipline model into an unlimited experience loss minimization problem with penalty factors, the improved support vector machine algorithm is used to construct and solve the objective kernel function. It is proved that the intelligent collection method based on big data mining algorithm model for the characteristics of applied mathematics in colleges and universities is effective. The sympathetic set corresponding to the flowing data is [115∼135]; the data similarity in big data environment is 1; in order to ensure that the intelligent collection method of applied mathematics discipline characteristics in colleges and universities based on big data analysis can collect data characteristics more accurately, which are 110/76.65/78/110, respectively.

Statistics and Analysis of Targeted Poverty Alleviation Information Integrated with Big Data Mining Algorithm

To gain a more comprehensive and systematic understanding of the impact of government assistance to poor households on poverty reduction targets, a targeted poverty alleviation information statistics and analysis integrated with big data mining algorithm is proposed. Combined with the big data knowledge of the new era, according to the machine learning (ML) pipeline module in spark, a big data computing framework, combined with known data mining algorithms, massive sample data are used to replace random stratified sampling data for modeling and analysis, and random forest model, logistic model, and newly proposed waterfall model are constructed for poor households. Finally, through the comparative evaluation of several poor household identification models, the results show that when 100 real data test the accuracy of the three poor household models, the random forest model and logistic model are slightly reduced, which are 82% and 72%, respectively, but the waterfall model is basically unchanged, which is 83%, and the three models have little change. The new waterfall design proposed in this article has the advantage of a high percentage of sample reuse and can effectively prevent overfitting, and there is no need for massive data. It is a stable and reliable new model. The combination of targeted poverty reduction algorithms and big information technology and mining data can get the most common causes more accurate and convincing results. The right rib trunk and rib are often separated from the common cause because of the population.

Multidimensional Information Network Big Data Mining Algorithm Relying on Finite Element Analysis.

In recent years, with the rapid development of the Internet, online social networks have been continuously integrated with traditional interpersonal networks and research on information dissemination in social networks has gradually increased. This article studies and analyzes the multidimensional information network big data mining algorithm based on the finite element analysis method. This paper firstly introduces the finite element analysis and calculation process, a finite element data mining simulation application software management system will integrate current data, calculation, and background data into one, then analyzes the data mining clustering algorithm, and conducts an experimental exploration of the influential node mining algorithm in complex networks. The experimental results show that the LIC algorithm is better than the CC algorithm, the DC algorithm, and the BC algorithm; its overall performance is improved by 30%, and the effect is better. The LIC algorithm can effectively and quickly determine the influential nodes, which is helpful for social network analysis.

Predicting Online Complaining Behavior in the Hospitality Industry: Application of Big Data Analytics to Online Reviews

Purpose: This study aims to enrich the published literature on hospitality and tourism by applying big data analytics and data mining algorithms to predict travelers’ online complaint attributions to significantly different hotel classes (i.e., higher star-rating and lower star-rating). Design/methodology/approach: First, 1992 valid online complaints were manually obtained from over 350 hotels located in the UK. The textual data were converted into structured data by utilizing content analysis. Ten complaint attributes and 52 items were identified. Second, a two-step analysis approach was applied via data-mining algorithms. For this study, sensitivity analysis was conducted to identify the most important online complaint attributes, then decision tree models (i.e., the CHAID algorithm) were implemented to discover potential relationships that might exist between complaint attributes in the online complaining behavior of guests from different hotel classes. Findings: Sensitivity analysis revealed that Hotel Size is the most important online complaint attribute, while Service Encounter and Room Space emerged as the second and third most important factors in each of the four decision tree models. The CHAID analysis findings also revealed that guests at higher-star-rating hotels are most likely to leave online complaints about (i) Service Encounter, when staying at large hotels; (ii) Value for Money and Service Encounter, when staying at medium-sized hotels; (iii) Room Space and Service Encounter, when staying at small hotels. Additionally, the guests of lower-star-rating hotels are most likely to write online complaints about Cleanliness, but not Value for Money, Room Space, or Service Encounter, and to stay at small hotels. Practical implications: By utilizing new data-mining algorithms, more profound findings can be discovered and utilized to reinforce the strengths of hotel operations to meet the expectations and needs of their target guests. Originality/value: The study’s main contribution lies in the utilization of data-mining algorithms to predict online complaining behavior between different classes of hotel guests.

Prediction method of financial market risk behavior based on big data mining algorithm

This paper predicts the default behavior of the financial market, reduces the bad debt rate in bank loans and securities investment, and discovers potential risks in time. The currently used technologies mainly rely on models with static weights, such as simple linear models. The advantage of these algorithms is that they are fast. But in a large number of samples, these algorithms also face inaccurate problems, requiring the use of machine learning modeling methods to train models. This paper proposes a modeling framework for financial data mining algorithms based on random forests, which can accurately predict microscopic behaviors and reduce financial risks. The experimental results show that the method proposed in this paper has certain application value, the prediction accuracy (precision) reaches 85%, and the recall rate (recall) reaches 90%.