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Forming coordinated teams that balance task coverage and expert workload

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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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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Research on Statistical Methods Related to the Added Value of Digital Economy Industry Based on data mining Algorithm 2

Research on Statistical Methods Related to the Added Value o...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Sun, Lijie Tianjin Agricultural University Tianjin China

ISBN: (纸本)9798350317558

As a new industry, digital economy industry is gradually becoming the main driving force of global economic growth. With the rapid development and popularization of information technology, the scale and influence of digital economy industry are expanding, and its contribution in added value is becoming increasingly significant. Therefore, the research on the added value of digital economy industry becomes particularly important. The purpose of this study is to deeply explore the formation mechanism, influencing factors and development trend of industrial added value in digital economy, and to provide scientific basis for policy making and industrial development. © 2024 IEEE.

关键词： Added value data mining algorithms Digital economy Industry Statistical methods statistical set theory

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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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Improved Instrument Approach Stability Analysis for Helicopters Using data Fusion and Analytics

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2022年第11期19卷 729-746页

作者： Robinson, Joseph N. Payan, Alexia P. Johnson, Charles Mavris, Dimitri N. Georgia Inst Technol Sch Aerosp Engn Aerosp Syst Design Lab 270 Ferst Dr Atlanta 303320150 Georgia FAA Sch Aerosp Engn Aerosp Syst Design Lab 270 Ferst Dr Atlantic City NJ 08405 USA Georgia Inst Technol Sch Aerosp Engn 270 Ferst Dr Atlanta 303320150 Georgia

Approach and landing are among the most challenging and dangerous tasks required of helicopter pilots. Helicopter accident statistics suggest that the majority of helicopter accidents occur during the approach and landing phases of flight. Unstabilized approaches are a leading cause of these accidents due to the increased potential for loss of control, loss of situational awareness, or controlled flight into terrain. In the present work, routine flight data records obtained as part of a voluntary helicopter flight data monitoring program are used to identify, analyze, and evaluate the stability of instrument approach events. A data fusion process is developed that enables detection of the approach phase, identification of an associated instrument approach procedure, analysis of approach stability, and knowledge discovery through data mining. The process identified 385 instrument approach events in a set of 7063 operational helicopter flight data records. Manual analysis of these events reveals some common errors, including crossing the final approach fix far below the assigned altitude and violating the minimum altitude requirement during initial and intermediate approach legs and course reversal maneuvers. data mining algorithms are tested and found to be capable of automatically identifying atypical or unstable approach events.

关键词： Rotor Systems Instrument Approach Procedure data Fusion Helicopter Flight data Monitoring data mining algorithms Controlled Flight into Terrain Instrument Flight Rules Helipad Philadelphia International Airport Rotorcrafts

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Research on Student Course Selection on Learning Behavior Analysis Algorithm Based on data mining 2

Research on Student Course Selection on Learning Behavior An...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Yu, Xiaoting Modern College of Northwest University Xi'an City China

ISBN: (纸本)9798350317558

With the popularization of higher education, the problem of students' course selection behavior has gradually become prominent. Based on the in-depth investigation of students' course selection behavior, this study identifies the main existing problems and puts forward corresponding countermeasures. It is found that students have blindness and utilitarianism in choosing courses, which leads to unreasonable knowledge structure and affects their studies. In view of these problems, this paper puts forward corresponding solutions to help students choose courses more scientifically. © 2024 IEEE.

关键词： data mining algorithms Pearson correlation coefficient theory similarity measurement algorithm Student behavior analysis student course selection algorithm

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Research on data mining Algorithm in the Optimization of Real Estate Management 2

Research on Data Mining Algorithm in the Optimization of Rea...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Wan, Jianguo Guangxi Polytechnic of Construction Nanning China

ISBN: (纸本)9798350317558

Management methods and objectives play an important role in real estate operation and management, but there is problems of inaccurate management. With the rapid development of the real estate market, the competition in the real estate industry is becoming increasingly fierce. Under this background, how to optimize real estate management through data analysis and improve enterprise operational efficiency and profitability has become the focus of real estate enterprises. From the perspective of real estate management, this paper discusses how to use data analysis technology to optimize, in order to improve the management level and competitiveness of real estate enterprises. © 2024 IEEE.

关键词： Business management data mining algorithms Management style Objective discussion Real estate statistical set theory

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Design and Implementation of Vulnerability Verification Framework Based on data mining Algorithm

Design and Implementation of Vulnerability Verification Fram...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Wu, Qiuyang Zhang, Liang Xu, Jingxuan Xu, Run Information Communication Branch of State Grid Anhui Electric Power Co. Ltd. Anhui Hefei230022 China

ISBN: (纸本)9798350383348

With the advent of big data era, data mining algorithms have been widely used in various fields. However, in the process of data processing, these algorithms may also have some loopholes, which lead to deviation or error of results. Therefore, it is very important to verify the vulnerability of data mining algorithm for improving the accuracy and reliability of the algorithm. Vulnerability verification of data mining algorithm is an important means to ensure the accuracy and reliability of the algorithm. Through comparative experiments, anomaly detection, fuzzy testing, cross-validation and interpretability checking, we can find and solve the loopholes in the algorithm. © 2024 IEEE.

关键词： computer technology data mining algorithms framework design and implementation vulnerability verification

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Application of Sentiment Analysis Algorithm based on Big data in Mental Health Assessment 3

Application of Sentiment Analysis Algorithm based on Big Dat...

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3rd International Conference on data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Deng, Qiyin Chengdu Agricultural College Chengdu City611130 China

ISBN: (纸本)9798331519032

Nowadays, college students are under increasing pressure. The accumulation of various pressures not only brings great harm to human body, but also brings many mental health (MH) problems. In this paper, large-scale Internet big data is taken as the research object, and the data mining algorithm based on heuristic clustering is studied. After text clustering, an evaluation index system based on text set is constructed based on the corresponding index of text set. The first-level evaluation indicators include emotional adjustment, learning influence, life influence and career influence. Using this model to evaluate the MH of college students can cluster the MH indicators of college students well in a large number of data, and the false positive rate and the false negative rate will not exceed 1%. This paper is helpful to the harmonious development of college students' MH. © 2024 IEEE.

关键词： data mining algorithms emotional regulation evaluation models learning impact psychological health of college students

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Research on Enterprise Marketing Resource Allocation Strategy Based on data mining Algorithm 3

Research on Enterprise Marketing Resource Allocation Strateg...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Zhang, Zifeng Shijiazhuang University of Applied Technology Hebei Shijiazhuang050081 China

ISBN: (纸本)9798350319125

In the enterprise, marketing can better make customers prefer this enterprise, and in this process, marketing resources are very important, due to the limited marketing resources, so reasonable allocation is an indispensable part. Ordinary resource allocation strategies do not better meet marketing expectations. analyze marketing resources, and indicators are divided according to resource allocation requirements to reduce resource allocation in the interfering factor. Then, artificial intelligence will form a resource allocation plan for the results of marketing resources for enterprises, and carry out the resource allocation results Comprehensive analysis. the data mining algorithm has an accurate effect on the resource allocation of enterprise marketing The rationality of resource allocation is better than that of ordinary resource allocation strategies. © 2023 IEEE.

关键词： artificial intelligence Corporate Marketing data mining algorithms Marketing distribution

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