Application Of Data Mining Algorithms Research Articles

Research on the application of data mining algorithm in photojournalism and short video communication

Short films and photojournalism are crucial parts of video communication, yet there are issues including poor video integration, inconsistency between short videos and photojournalism, and slow communication speed. In order to evaluate and analyse video communication, this research offers a data mining algorithm. First, the database's images and videos are chosen and analysed using a mining algorithm, and then the indicators are reduced by dividing them into groups based on the specifications for video dissemination. video transmission distractions. The mining algorithm then analyses the video propagation, creates a plan that satisfies the criteria, and refines the plans that satisfies the criteria. Analyse. The integration level, timeliness, and compliance rate of data mining algorithms for video propagation are superior to traditional video dissemination method under specific analysis criteria, according to MATLAB simulation.

Morphological characterization of the Polatli sheep in terms of live weight using data mining algorithms.

The aim of this research is both to estimate the live weight (LW) of Polatli sheep (Ile de France × Akkaraman (G1)) by considering some body measurements (withers height (WH), rump height (RH), body length (BL), chest depth (CD), chest width (CD), chest girth (CG), cannon bone circumference (CBC)), age, and sex variables as independent variables using C&RT (Classification and Regression Tree), CHAID (Chi-square Automatic Interaction Detector), and MARS (Multivariate Adaptive Regression Splines) algorithms and to determine the significant independent variables in the estimation of live weight. For this purpose, a total of 210 sheep were used, including 180 females and 30 males of different ages, for the estimation of LW. The calculated Pearson correlation coefficients between LW and WH, RH, BL, CD, CW, CG, and CBC characteristics are 0.897, 0.896, 0.853, 0.948, 0.550, 0.914, and 0.798, respectively (p < 0.05). In the application of data mining algorithms as prediction models, a cross-validation of 10 was used, while for tree-based algorithms, the parent node was set to 10 and the child node to 5. While CHAID and C&RT algorithms each used 8 independent variables to explain the variation observed in LW, the MARS algorithm used 9 independent variables. In Polatli sheep, the sheep with the highest live weight was found in the node with age > 3 and CD > 36 cm cutting point in the CHAID algorithm (93.571 kg). In the C&RT algorithm, it was predicted to be (91.316 kg) when age > 0, CD > 36.5 cm, and CBC > 9.5 cm. When evaluated considering commonly used criteria, the prediction performances of CHAID, C&RT, and MARS algorithms were calculated as follows: RMSE (root mean square error) values are "5.788, 5.103, 4.005"; SDR (standard deviation ratio) values are "0.254, 0.224, 0.176"; MAPE (mean absolute percentage error) values are "7.555, 6.675, 5.682"; Adj-Rsq (adjusted R-squared) values are "0.935, 0.950, 0.969"; and AIC (Akaike information criterion) values are "741.436, 688.489, 582.792," respectively. In terms of prediction performance, among the tree algorithms (CHAID and C&RT), C&RT was found to be the best, while considering all performance measures, it was observed that the MARS algorithm exhibited the best performance. Consequently, it has been determined that C&RT and MARS algorithms can be safely employed in morphological characterization studies for the identification of indirect criteria and the formation of elite herds in terms of LW. This decision allows for the reliable use of these algorithms to facilitate the selection of indirect variables and the establishment of elite populations in breeding programs focusing on live weight.

A Study on Optimizing Error Detection and Correction Strategies in Physical Education and Sport Teaching using Data Mining Algorithms

In the fiercely competitive realm of sports and physical education, the application of data mining algorithms has emerged as a vital solution. Machine learning has streamlined processes, offering a seamless means of elevating the quality of education and training provided to students, particularly in the context of sports. This technological support empowers the sports education system to make more informed decisions pertaining to the physical development of aspiring athletes. In this comprehensive study, a blended approach of qualitative methods has been leveraged to gather intricate insights, enriching the overall understanding of the subject. Additionally, an in-depth exploration of articles and journals has been undertaken to scrutinize the practical implementation of data algorithm techniques geared towards enhancing physical training. The resultant findings underscore a substantial and tangible nexus between data algorithms and the domain of sports education. Of paramount significance is the central role played by data mining algorithms in augmenting performance. Notably, the National Sports Board (NSB) has extensively harnessed this technology to meticulously monitor players' on-field performance, ultimately leading to a granular comprehension of each player's capabilities. This paper emphasizes the methods of optimizing mistake detection and its joining systems for increasing the punishment in the operational procedures.

Big Data Algorithm and Zhejiang Opera Culture Gene Mapping

Abstract This paper proposes the design of the Zhejiang opera culture gene map using the Zhejiang opera culture gene and gene map. Secondly, it focuses on the application of data mining algorithms in managing opera culture resources, including indexing and querying of dissemination track data and mining Zhejiang opera culture data resources. Finally, the application effect of data mining in Zhejiang opera culture gene mapping is demonstrated through an example analysis. The results show that for the multi-valued attribute dataset of trajectory data of Zhejiang opera culture, the execution time of the optimized algorithm is reduced by 848 ms under a 1% support rate and 88 ms under a 10% support rate compared with the Apriori algorithm. This paper reveals the inner connections and characteristics of Zhejiang opera culture through a data mining algorithm for cultural gene mapping, providing a scientific basis.

Application of data mining algorithms to study data trends for corneal transplantation

To utilize data mining for analysis of corneal transplantations (CT) in Florida from 2005-2014, segmented by demographics, geography, and transplantation technique. A retrospective, database study was performed utilizing data queried from the Healthcare and Cost Utilization Project using Current Procedural Terminology codes for lamellar keratoplasty (ALK), endothelial keratoplasty (EK), and penetrating keratoplasty (PKP). Payer status, ethnic group, age, gender, and geography (urban versus rural) was extracted from each surgical encounter and reconfigured to provide a "clean", congruous dataset for statistical analysis. This Institutional Review Board-approved study did not utilize identifiable patient information; thus, individual informed consent was not required. From 2005-2014, CT (n=28,607) represented less than 1% of the total ambulatory surgeries (n=12,695,932) performed in Florida. EK volume increased while PKP and ALK volume decreased, year-over-year. Statistical significance was found between transplantation technique by sex (P<0.001) and ethnic group (P<0.001). The largest sex discrepancy was EK (59% female, 41% male). White patients underwent relatively fewer PKP than EK (71% vs. 83% of totals), while Black patients underwent relatively more PKP than EK (14% vs 6% of totals). Statistical significance was found between techniques by payer (P<0.001). Medicare was the most common payer for all techniques, but ALK and PKP had higher percentages of private insurance and self-pay. No statistical significance was found between techniques by geographic location. Corneal edema (22.4%), endothelial dystrophy (17.5%), and bullous keratopathy (10.9%) were erroneously coded as indications for ALK. Corneal scars (2.5%) and corneal opacity (1.7%) were erroneously coded as indications for EK. CT rates in Florida appear to overrepresent the female sex and underrepresent ethnic minorities, with propensities between PKP and African Americans, EK and female patients, and EK and Medicare reimbursement. Our study further confirms the utility of data mining for providing efficient, detailed, and practical insights into ophthalmology procedures, while highlighting the intrinsic challenges of large datasets.

Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review.

Big data in health care is a fast-growing field and a new paradigm that is transforming case-based studies to large-scale, data-driven research. As big data is dependent on the advancement of new data standards, technology, and relevant research, the future development of big data applications holds foreseeable promise in the modern day health care revolution. Enormously large, rapidly growing collections of biomedical omics-data (genomics, proteomics, transcriptomics, metabolomics, glycomics, etc.) and clinical data create major challenges and opportunities for their analysis and interpretation and open new computational gateways to address these issues. The design of new robust algorithms that are most suitable to properly analyze this big data by taking into account individual variability in genes has enabled the creation of precision (personalized) medicine. We reviewed and highlighted the significance of big data analytics for personalized medicine and health care by focusing mostly on machine learning perspectives on personalized medicine, genomic data models with respect to personalized medicine, the application of data mining algorithms for personalized medicine as well as the challenges we are facing right now in big data analytics.

Research and Application of the Data Mining Technology in Economic Intelligence System.

In the context of the rapid development of the modern economy, information is particularly important in the economic field, and information determines the decision-making of enterprises. Therefore, how to quickly dig out information that is beneficial to the enterprise has become a crucial issue. This topic applies data mining technology to economic intelligent systems and obtains the data object model of economic intelligent systems through the integration of information. This article analyzes the interrelationship between its objects on this basis and uses data mining-related methods to mine it. The establishment of economic intelligence systems not only involves the establishment of mathematical models of economic systems, but also includes research on the algorithms applied to them. How to apply an algorithm to quickly and accurately extract the required economic intelligence domain information from the potential information in the database, or to provide a method to find the best solution, involves the use of association rules and classification prediction methods. The application of data mining algorithms can be used to study the application of economic intelligence systems. This paper develops and designs an economic intelligence information database and realizes the economic intelligence system on this basis, and realizes the research results. Finally, this paper has tested the dataset, and the results show that the classification accuracy of this algorithm is 2.64% higher than that of the ID3 algorithm.

An application of data mining algorithms for predicting factors affecting Big Data Analysis adoption readiness in SMEs.

The adoption of Big Data Analysis (BDA) has become popular among firms since it creates evidence for decision-making by managers. However, the adoption of BDA continues to be poor among small and medium enterprises (SMEs). Therefore, this study adopted the Technology-Organization-Environment (TOE) framework to identify the drivers of readiness to adopt BDA among SMEs. Chi-square automatic interaction detection (CHAID), Bayesian network, neural network, and C5.0 algorithms of data mining were utilized to analyze data collected from 240 Vietnamese managers of SMEs. The evaluation model identified the C5.0 algorithm as the best model, with accurate results for the prediction of factors influencing the readiness to adopt BDA among SMEs. The findings revealed management support, data quality, firm size, data security and cost to be the fundamental factors influencing BDA adoption readiness. Moreover, the results identified the service sector as having a higher level of readiness toward the adoption of BDA compared to the manufacturing sector. The findings are imperative for the enhancement of the decision-making process and advancement of comprehension of the determinants of BDA adoption among SMEs by researchers, managers, providers and policymakers.

Application of Data Mining Algorithms in Data Analysis of Information Education Evaluation

In the new century, China has attached great importance to informatization work. Based on the development of education and teaching, the necessary infrastructure for informatization must be gradually improved. At the same time, we should make reasonable and effective use of information technology in the analysis of educational evaluation data, so that informatization teaching becomes an indispensable part of education. The purpose of this article is to study the application of data mining algorithms in data analysis of information education evaluation. This article will give full play to the interactive, dynamic and open advantages of the teaching resources of the network platform, combined with the ability of teachers to use information technology, rationally optimize the education evaluation data analysis system, use existing information technology to build an evaluation system, and evaluate the participating teachers. Ensure that the evaluation results are objective and effective. According to the overall framework and demand analysis of the system, the teacher information technology application ability assessment system is divided into different functional modules, each module is analyzed in detail according to user needs, and the corresponding database is designed. The evaluation system has an intuitive user interface, easy operation, and stable operation. It is suitable for first-line teachers and school administrators in primary and secondary schools to make a fair assessment of the teaching of teachers. Through the evaluation and analysis report, teachers can optimize and comprehensively understand their own teaching situation, see the problems in their teaching, and change the teaching method in time; appropriately summarize and analyze to improve the teaching level. Understand the teaching work of teachers, and realize the in-depth integration of informatization and teaching. Experimental investigations have shown that students have a great lack of experimental ability. The other four abilities are not far apart, all of which are above 70%. They are in the upper middle and upper reaches. Among them, the analysis and application tools are about 5% higher than the grade level. In general, the experimental ability must be strengthened to enhance. Let students achieve all-round development. Through the evaluation and analysis report, teachers can find their own shortcomings in teaching more clearly based on their optimization, and change the teaching methods in time; appropriately summarize and analyze to improve the teaching level.

Application of data mining algorithm in correlation analysis of the driving factors for alpine grassland degradation

Application of data mining algorithm in correlation analysis of the driving factors for alpine grassland degradation

Cross-border E-commerce Course Construction Based on Data Mining Algorithm

In recent years, Cross-border(CB) e-commerce(EC) has developed rapidly in our country, and the government has also introduced a series of measures to promote the development of CB EC. In the era of big data, recommendation systems are widely used in CB EC platforms, and more and more attention is paid to its personalized recommendation technology. Based on this, this paper studies the application of data mining algorithms in the construction of CB EC courses, which has certain guiding significance for the current market and enterprise development. By taking two CB EC companies as the research object, this paper uses data mining algorithms to mine and analyze the relevant data of these two EC companies, and find that a platform with a data mining system has more advantages in researching customer shopping preferences;comparing people’s satisfaction with EC companies with data mining systems and traditional companies, 83% of the participants in the experimental group were satisfied, of which 60% were very satisfied, while only 59% in the control group of people are satisfied.This shows that data mining algorithms are of great significance to the development of CB EC, and it also points out a new direction for the construction of CB EC courses, such as CB EC based on data mining algorithms. On the one hand, this will also play a positive role in the future development of CB EC.

Data Mining of Enterprise Financial Management Based on AHP

With the rapid development of information technology, enterprises are increasingly dependent on information technology in the development process. In the process of production and operation, enterprises usually accumulate massive data, including internal cost data, investment data, financing data and asset allocation data, as well as external customer and supplier data. These two kinds of data are directly related to the financial management status and marketing planning means of enterprises. Taking data mining algorithm as an example, this paper introduces the categories of data mining algorithm. Then the application of data mining algorithm in enterprise financial management and marketing planning is described. Analytic hierarchy process (AHP) is a multi criteria decision-making method which combines qualitative and quantitative methods. It can express and deal with subjective judgment in quantitative form. In this paper, the analytic hierarchy process (AHP) is used to improve the data mining technology, and further broaden the types of data mining algorithm model. This method improves the efficiency and effectiveness of enterprise financial management, and provides corresponding reference for the development of enterprises.

Applying data mining algorithms to real estate appraisals: a comparative study

Purpose Real estate appraisals are becoming an increasingly important means of backing up financial operations based on the values of these kinds of assets. However, in very large databases, there is a reduction in the predictive capacity when traditional methods, such as multiple linear regression (MLR), are used. This paper aims to determine whether in these cases the application of data mining algorithms can achieve superior statistical results. First, real estate appraisal databases from five towns and cities in the State of Paraná, Brazil, were obtained from Caixa Econômica Federal bank. Design/methodology/approach After initial validations, additional databases were generated with both real, transformed and nominal values, in clean and raw data. Each was assisted by the application of a wide range of data mining algorithms (multilayer perceptron, support vector regression, K-star, M5Rules and random forest), either isolated or combined (regression by discretization – logistic, bagging and stacking), with the use of 10-fold cross-validation in Weka software. Findings The results showed more varied incremental statistical results with the use of algorithms than those obtained by MLR, especially when combined algorithms were used. The largest increments were obtained in databases with a large amount of data and in those where minor initial data cleaning was carried out. The paper also conducts a further analysis, including an algorithmic ranking based on the number of significant results obtained. Originality/value The authors did not find similar studies or research studies conducted in Brazil.

Research and application of data mining algorithm

With the breakthrough development of a series of technologies such as machine learning and internet of things, data plays an increasingly important role in everyone’s daily affairs and work. With the explosive exponential growth of data volume, data types are also quietly changing, from the traditional single, structured data to today’s diverse, semi-structured data. In the face of emerging new massive data, data mining technology has gradually become the focus of attention. Data mining was also known as knowledge discovery (KDD) at that time. It was mainly defined as a pattern hidden in massive data, which must be understood by people and bring potential benefits. In this paper, we mainly studies the basic principle and algorithm knowledge of data mining, and applies ridge regression and random forest algorithm model in real estate price forecasting. Finally, through the stacking thinking of ensemble learning in ensemble learning, we propose a fusion method of ridge regression and random forest model, and obtain a more accurate and stable prediction model.

Sports result prediction using data mining techniques in comparison with base line model

Sports prediction is one of the recent growing areas of interest entailing good prediction accuracy. Coaches require models in order to assess their players, analyse opponent teams and formulate winning strategies. Generation of comprehensive statistical data in sports has enabled data mining (DM) techniques to be applied to it in order to extract underlying predictive information. In this paper, an approach based on data mining is proposed for result prediction in sports. The work includes pre-processing of data, feature extraction, attribute selection and application of DM algorithms as a learning strategy. To validate our proposed model, a case study concerning prediction of the results of Indian Premier League (IPL) matches is illustrated. The constructed models are based on the performance of teams in past matches, player performance indices, opposition team information and external factors, and therefore, relevant features are engineered to indicate the same. The best prediction accuracy was found to be 70.58%.

Application of data mining algorithms for improving stress prediction of automobile drivers: A case study in Jordan

Driving daily through traffic congestion has been recognised as a major cause of stress. High levels of stress while driving negatively impact the driver's decisions which could potentially lead to accidents and other long-term health hazards. Accordingly, there is a great need to determine stress levels for drivers based on measuring and predicting the major causes (features or classes) that increase stress levels.In this paper, the problem of predicting automobile drivers' stress levels, as experienced during actual driving, is investigated through the application of five different data mining algorithms, namely K-Nearest Neighbour (KNN), Decision Tree (J48), Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Networks (ANN). An experiment was conducted on 14 drivers taking various routes in Amman – Jordan, with a wearable biomedical device attached to the driver to instantly collect physiological data. The collected data (dataset) is grouped into two different categories, namely ‘Yes’ to signify the presence of stress and ‘No’ to signify the absence of stress.In order to efficiently apply data mining algorithms to the data set, oversampling was used to avoid the negative effect of driver samples with a lesser class on the prediction of stress.The findings are evaluated in relation to stress prediction and accordingly contrasted alongside standard reference approaches that do not consider oversampling and/or feature selection using the Friedman rank test. The proposed approach, in combination with RF, was seen to surpass any others in terms of accuracy, AUC, specificity, and sensitivity. The accuracy, AUC, specificity, and sensitivity rates produced by RF utilising our proposed approach were 98.92%, 99.91%, 98.46%, and 99.36%, respectively.

Data mining tools

The development and application of data mining algorithms requires the use of powerful software tools. With challenges such as big data encountered in economy or gene sequencing for life science, data mining is important for daily problems as well as specialized fields. However, the large variety of requirements and user groups lead to a huge number and diversity of software tools. We give an overview by discussing the historical development and presenting a range of existing state‐of‐the‐art data mining and related tools. This paper is an update of our previous article from 2011 following the encyclopedic aspect of Wiley Interdisciplinary Reviews to include new findings or references and changing outdated information. However, since the paper should be able to stand alone, it includes many still valid elements of the previous article. Following the original paper, we propose criteria for the tool categorization based on different user groups, data structures, data mining tasks and methods, visualization and interaction styles, import and export options for data and models, platforms, and license policies. These criteria are then used to classify data mining tools into nine different categories. The typical characteristics of these types are explained and a selection of the most important tools is categorized.This article is categorized under: Application Areas &gt; Data Mining Software Tools

Application of data mining algorithms in the management of the broiler production

Este trabalho tem por objetivo analisar a aplicacao de algoritmos de mineracao de dados para predicao e classificacao de variaveis produtivas que compoem as caracteristicas das aves produzidas por uma cooperativa localizada na regiao oeste paranaense. A base de dados disponibilizada pela empresa apresenta um historico de movimentacao de 2 anos, contendo as principais variaveis de producao, desde o alojamento ate a fase de abate. Tres algoritmos de analise de dados, do software WEKA, foram utilizados na implementacao dos modelos de predicao e classificacao. Para implementacao dos modelos foram utilizados dados de 6000 lotes de aves das linhagens Coob e Coob Slow, obtidos do banco de dados da cooperativa, no periodo compreendido entre 01/07/2014 e 31/07/2016. Os resultados obtidos mostram que os modelos de predicao fornecem estimativas confiaveis para as variaveis de resposta: Peso Medio e Indice de Eficiencia Produtiva e o modelo de classificacao apresenta um bom desempenho na classificacao das linhagens das aves.

Application of Multidimensional Association Mining in Shield Tunneling Optimization

The rapid development of data science promotes the innovative application of data mining algorithms in geotechnical engineering. So the multidimensional association algorithm is tried in the technical optimization of shield tunneling. Based the original data sets on geological condition, tunneling operation and monitoring, the data preprocessing is carried out according to such steps as sorting and statistics, classification and coding, factor selection and data discretization. Then eight important parameters are selected for association analysis, including tunneling layer, surface settlement, and six operating parameters of the shield machine. Apriori algorithm is used for mining the association rules to establish the association models of those parameters, whose rationality is tested. Also the engineering significance of the multi-parameter association models is explained in the view of practical experience. Finally, the data mining principles of the shield tunneling parameters association rules are discussed. As a simple and reasonable data analysis method, the association algorithm provides a new way for the intelligent construction of shield tunneling.

Preparation and Cluster Analysis of Data from the Industrial Production Process for Failure Prediction

Abstract This article is devoted to the initial phase of data analysis of failure data from process control systems. Failure data can be used for example to detect weak spots in a production process, but also for failure prediction. To achieve these goals data mining techniques can be used. In this article, we propose a method to prepare and transform failure data from process control systems for application of data mining algorithms, especially cluster analysis.