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Correlation Analysis Between Student Psychological State and Grades Based on data mining algorithms

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第2期15卷 49-58页

作者： Zeng Daoyan Chen Disi Sichuan Huaxin Modern Vocat Coll Chengdu 610000 Sichuan Peoples R China

As society has evolved and educational reform has become more profound, the psychological state and academic performance of vocational college students have become the focus of attention for educators. This study aims to construct a correlation model between the positive psychological state and academic performance of vocational college students based on data mining algorithms to offer a conceptual foundation and practical guidance for the optimization of vocational education. The relationship between positive psychological state and academic performance was analyzed through a literature review, as well as the application of data mining algorithms in the field of education. A certain amount of data on vocational college students was collected using questionnaire surveys and empirical research methods, including their basic information, positive psychological status indicators, and academic performance data. Subsequently, data mining algorithms were used to preprocess and analyze the collected data, and a correlation model between the positive psychological state and academic performance of vocational college students was constructed. Finally, through validation and evaluation of the model, it was found that there is a significant positive correlation between positive psychological state and academic performance, and the model has high predictive accuracy. The study's results suggest that the positive psychological state of vocational college students has a significant impact on their academic performance. Educators should consider students' mental health and take effective measures to enhance their positive psychological state, thereby improving their academic performance. This study provides a new research perspective and method for the field of vocational education, which helps to promote the development and reform of vocational education.

关键词： data mining algorithms vocational students positive psychological state academic performance correlation model

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Novel Abnormal data mining and Detection Algorithm for Intelligent Systems 4

Novel Abnormal Data Mining and Detection Algorithm for Intel...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Xu, Shaomei Fan, Yanling Lin, Bing Continuing Education Service Center Shandong Institute of Commerce and Technology Shandong Jinan250103 China

ISBN: (纸本)9798331523923

In intelligent systems, as the amount of data increases, how to analyse data and extract abnormal information is an important task. Based on this, in order to improve the detection efficiency and accuracy of abnormal data in intelligent systems, this study proposes a new and efficient abnormal data mining and detection algorithm. In the initial modelling process, this study introduced the minimum modification cost principle based on cross entropy for modelling. This algorithm optimizes the time series data cleaning process, thereby removing noise data while maintaining the integrity of the data sequence. Then, the extreme gradient boosting algorithm is combined with the Gaussian kernel function to achieve data dimension reduction. This operation can reduce data dimensions and feature redundancy. Finally, the overall anomaly detection operation by integrating the improved Ada boost algorithm with the sampling balance technology can improve the accuracy of the classifier while solving the problem of unbalanced data. The experimental results show that the proposed method is superior to the traditional algorithms in comprehensive indicators. © 2025 IEEE.

关键词： Abnormal data Detection data mining algorithms Efficiency Optimization Intelligent Systems

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Forming coordinated teams that balance task coverage and expert workload

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data mining AND KNOWLEDGE DISCOVERY 2025年第3期39卷 1-37页

作者： Vombatkere, Karan Gionis, Aristides Terzi, Evimaria Boston Univ Dept Comp Sci Boston MA 02215 USA KTH Royal Inst Technol Div Theoret Comp Sci Stockholm Sweden

We study a new formulation of the team-formation problem, where the goal is to form teams to work on a given set of tasks requiring different skills. Deviating from the classic problem setting where one is asking to cover all skills of each given task, we aim to cover as many skills as possible while also trying to minimize the maximum workload among the experts. We do this by combining penalization terms for the coverage and load constraints into one objective. We call the corresponding assignment problem Balanced-Coverage, and show that it is NP\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textbf{NP}$$\end{document}-hard. We also consider a variant of this problem, where the experts are organized into a graph, which encodes how well they work together. Utilizing such a coordination graph, we aim to find teams to assign to tasks such that each team's radius does not exceed a given threshold. We refer to this problem as Network-Balanced-Coverage. We develop a generic template algorithm for approximating both problems in polynomial time, and we show that our template algorithm for Balanced-Coverage has provable guarantees. We describe a set of computational speedups that we can apply to our algorithms and make them scale for reasonably large datasets. From the practical point of view, we demonstrate how to efficiently tune the two parts of the objective and tailor their importance to a particular application. Our experiments with a variety of real-world datasets demonstrate the utility of our problem formulation as well as the efficiency of our algorithms in practice.

关键词： Team formation Submodular optimization Greedy Social network data mining algorithms

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Fast algorithms for Frequent Itemset mining from Uncertain data 14

Fast Algorithms for Frequent Itemset Mining from Uncertain D...

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14th IEEE International Conference on data mining (IEEE ICDM)

作者： Leung, Carson Kai-Sang MacKinnon, Richard Kyle Tanbeer, Syed K. Univ Manitoba Dept Comp Sci Winnipeg MB Canada

ISBN: (纸本)9781479943036

The majority of existing data mining algorithms mine frequent itemsets from precise data. A well-known algorithm is FP-growth, which builds a compact FP-tree structure to capture important contents of precise data and mines frequent itemsets from the FP-tree. However, there are situations in which data are uncertain. To capture important contents (e.g., existential probabilities) of uncertain data for mining frequent itemsets, the UF-growth algorithm uses a UF-tree structure. However, the UF-tree can be large. Other tree structures for handling uncertain data may achieve compactness at the expense of looser upper bounds on expected supports. To solve this problem, we propose fast algorithms that use compact tree structures for capturing uncertain data with tightened upper bounds to expected support (tube) for frequent itemset mining from uncertain data. Experimental results show the tightness of tube provided by our algorithms and the compactness of our tree structures.

关键词： Association analysis data mining algorithms expected support frequent patterns tree structures uncertain data

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Integration of software-based cognitive approaches and brain-like computer machinery for efficient cognitive computing

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Neuroscience Informatics 2025年第2期5卷 100194-100194页

作者： Banchhor, Chitrakant Rawat, Manoj Kumar Joshi, Rahul Dhabliya, Dharmesh Kulkarni, Omkaresh Pande, Sandeep Dwarkanath Pawar, Umesh Computer Science & Engg. (AI) Department Vishwakarma Institute of Information Technology Pune India Medicaps university Indore India CSE Department Symbiosis Institute of Technology Pune Symbiosis International (Deemed University) Pune Maharashtra India Department of Information Technology Vishwakarma Institute of Technology Maharashtra Pune India Computer Science & Engg. (AIML) Department Vishwakarma Institute of Information Technology Pune India School of Computer Engineering MIT Academy of Engineering Alandi Maharashtra Pune India School of Computer Science and Engineering Sandip University MS Nashik India

The widespread adoption of the Internet has transformed various industries, driving significant systemic reforms across different sectors. This transformation has enhanced the Internet's role in information dissemination, resource sharing, and global connectivity, allowing for more efficient distribution of knowledge and services. The development of the Internet model and its research bring significant benefits from the network, enabling people to use and learn from it. However, the traditional education model provides only limited knowledge, restricting growth and progress. Moreover, there is a vast world of knowledge yet to be explored. Nowadays, with the help of network tools, people can understand the dynamics of the whole world and accept the culture and knowledge of different regions without going out. Throughout the study of English legacy problems in various countries, efficient learning methods and high levels of English skills are the goals pursued, while the traditional English model can't meet the students' learning needs in a short time. The model construction of data mining algorithm based on large open network courses is a model for solving legacy problems adopted both domestically and internationally. According to the survey data of universities in various countries, the use of data mining algorithm can fundamentally meet the student's desire and demand for English knowledge. This research, integrates the mining algorithm into English research, which will essentially improve the English legacy problems. © 2025 The Author(s)

关键词： data mining algorithms English learning Legacy problems Multiple intelligences theory

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data mining model adjustment control charts for cascade processes

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EUROPEAN JOURNAL OF INDUSTRIAL ENGINEERING 2013年第4期7卷 442-455页

作者： Kim, Seoung Bum Jitpitaklert, Weerawat Chen, Victoria C. P. Lee, Jinpyo Park, Sun-Kyoung Korea Univ Sch Ind Management Engn Seoul 136713 South Korea Univ Texas Arlington Dept Ind & Mfg Syst Engn Arlington TX 76019 USA Hongik Univ Sch Business Seoul 121791 South Korea Hanyang Cyber Univ Sch Business Adm Seoul 133791 South Korea

Control charts have been widely recognised as important tools in system monitoring of abnormal behaviour and quality improvement. Traditional control charts have a major assumption that successive observations are uncorrelated and normally distributed. When this assumption is violated, the traditional control charts do not perform well, but instead show increased false alarm rates. In this study, we propose a data mining model adjustment control chart to address autocorrelation problems for cascade processes. The basic idea of the proposed control chart is to monitor the residuals obtained by data mining models. The data mining models used in this study include support vector regression and artificial neural networks. A simulation study was conducted to evaluate the performance of the proposed control chart and compare it with the standard regression adjustment control chart and the observations-based control chart in terms of average run length performance. The results showed that the proposed data mining model adjustment control charts yielded better performance than the two other methods considered in this study.

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A new integrated data mining model to map spatial variation in the susceptibility of land to act as a source of aeolian dust

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2020年第33期27卷 42022-42039页

作者： Gholami, Hamid Mohammadifar, Aliakbar Pourghasemi, Hamid Reza Collins, Adrian L. Univ Hormozgan Dept Nat Resources Engn Bandar Abbas Hormozgan Iran Shiraz Univ Coll Agr Dept Nat Resources & Environm Engn Shiraz Iran Rothamsted Res Sustainable Agr Sci Dept Okehampton EX20 2SB Devon England

This research developed a more efficient integrated model (IM) based on combining the Nash-Sutcliffe efficiency coefficient (NSEC) and individual data mining (DM) algorithms for the spatial mapping of dust provenance in the Hamoun-e-Hirmand Basin, southeastern Iran. This region experiences severe wind erosion and includes the Sistan plain which is one of the most PM2.5-polluted regions in the world. Due to a prolonged drought over the last two decades, the frequency of dust storms in the study area is increasing remarkably. Herein, 14 factors controlling dust emissions (FCDEs) including soil characteristics, climatic variables, digital elevation map, normalized difference vegetation index, land use and geology were mapped. Correlation and collinearity among the FCDEs were examined by the Pearson test, tolerance coefficient (TC) and variance inflation factor (VIF), with the results suggesting a lack of collinearity between FCDEs. A tree-based genetic algorithm was applied to prioritize and quantify the importance weights of the FCDEs. Thirteen individual data mining models were applied for mapping dust provenance. The model performance was assessed using root mean square error, mean absolute error and NSEC. Based on clustering analysis, the 13 DM models were grouped into five clusters and then the cluster with the highest NSEC values used in an integrated modelling process. Based on the results, the IM (NSEC = 93%) outperformed the individual DM models (the NSEC values range between 51 and 92%). Using the IM, 11, 5, 7 and 77% of the total study area were classified into low, moderate, high and very high susceptibility classes for dust provenance, respectively. Overall, the results illustrate the benefits of an IM for mapping spatial variation in the susceptibility of catchment areas to act as dust sources.

关键词： Dust provenance susceptibility Integrated modelling Cluster analysis data mining algorithms

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Short-Term Prediction of Wind Farm Power: A data mining Approach

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IEEE TRANSACTIONS ON ENERGY CONVERSION 2009年第1期24卷 125-136页

作者： Kusiak, Andrew Zheng, Haiyang Song, Zhe Univ Iowa Intelligent Syst Lab Dept Mech & Ind Engn Iowa City IA 52242 USA

This paper examines time series models for predicting the power of a wind farm at different time scales, i.e., 10-min and hour-long intervals. The time series models are built with data mining algorithms. Five different data mining algorithms have been tested on various wind farm datasets. Two of the five algorithms performed particularly well. The support vector machine regression algorithm provides accurate predictions of wind power and wind speed at 10-min intervals up to I h into the future, while the multilayer perceptron algorithm is accurate in predicting power over hour-long intervals up to 4 h ahead. Wind speed can be predicted fairly accurately based on its historical values;however, the power cannot be accurately determined given a power curve model and the predicted wind speed. Test computational results of all time series models and data mining algorithms are discussed. The tests were performed on data generated at a wind farm of 100 turbines. Suggestions for future research are provided.

关键词： data mining algorithms multiperiod prediction multiscale prediction time series model wind farm power prediction

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An effective density-based clustering and dynamic maintenance framework for evolving medical data streams

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INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS 2019年第Jun.期126卷 176-186页

作者： Al-Shammari, Ahmed Zhou, Rui Naseriparsaa, Mehdi Liu, Chengfei Swinburne Univ Technol Fac Sci Engn & Technol Dept Comp Sci & Software Engn Melbourne Vic Australia Univ Al Qadisiyah Al Diwaniyah Iraq

Background: Medical data stream clustering has become an integral part of medical decision systems since it extracts highly-sensitive information from a tremendous flow of medical data. However, clustering and maintaining of medical data streams is still a challenging task. That is because the evolving of medical data streams imposes various challenges for clustering such as the ability to discover the arbitrary shape of a cluster, the ability to group data streams without a predefined number of clusters, and the ability to maintain the data clusters dynamically. Objective: To support the online medical decisions, there is a need to address the clustering challenges. Therefore, in this paper, we propose an effective density-based clustering and dynamic maintenance framework for grouping the patients with similar symptoms into meaningful clusters and monitoring the patients' status frequently. Methods: For clustering, we generate a set of initial medical data clusters based on the combination of Piece-wise Aggregate Approximation and the density-based spatial clustering of applications with noise called (PAA + DBSCAN) algorithm. For maintenance, when new medical data streams arrive, we maintain the initially generated medical data clusters dynamically. Since the incremental cluster maintenance is time-consuming, we further propose an Advanced Cluster Maintenance (ACM) approach to improve the performance of the dynamic cluster maintenance. Results: The experimental results on real-world medical datasets demonstrate the effectiveness and efficiency of our proposed approaches. The PAA + DBSCAN algorithm is more efficient and effective than the exact DBSCAN algorithm. Moreover, the ACM approach requires less running time in comparison with the Baseline Cluster Maintenance (BCM) approach using different tuning parameter values in all datasets. That is because the BCM approach tracks all the data points in the cluster. Conclusion: The proposed framework is capable of clust

关键词： Density-based clustering Dynamic maintenance data mining algorithms Medical data streams

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Probabilistic REpresentatives mining (PREM): A Clustering Method for Distributional data Reduction

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AIAA JOURNAL 2022年第4期60卷 2580-2596页

作者： Gao, Zhenyu Puranik, Tejas G. Mavris, Dimitri N. Georgia Inst Technol Sch Aerosp Engn Atlanta GA 30332 USA

Complex computations and analyses on massive data sets can be impractical or infeasible. data reduction is a crucial problem in the era of big data to obtain a reduced representation of the data set to facilitate more efficient yet accurate analyses. To best preserve the integrity of the original data set, a reduced representation aims to best maintain the same data distribution, referred to as a probabilistically representative subset. This paper considers the problem of reducing a large data set to very small such subsets at which random sampling does not perform well enough. We propose a data mining approach called Probabilistic Representatives mining (PREM) to tackle this challenge. PREM uses balanced clustering to prevent undersampling and oversampling issues and multistage computing strategy to achieve better scalability and consistency. Numerical experiments on typical probability distributions and real-world data sets in the field of aeronautics and astronautics demonstrate PREM's superiority over the baselines. An uncertainty quantification case study from aviation environmental impact modeling further shows PREM's effectiveness and accuracy in generating probabilistically representative small samples for costly computations. Potential limitations and extensions of the method are also discussed in the paper.

关键词： data Reduction Computing Strategies Continuous Probability Distributions data mining algorithms Air Transportation System Astronautics Aeronautics Aviation Environmental Design Tool Radial Basis Function Kernel LANDSAT

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