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5th Asia Energy and Electrical Engineering Symposium (AEEES)

作者： Li, Shan Lu, Xin Ouyang, Jianna Zhou, Yangjun Zhang, Wei Guangxi Power Grid Co Ltd Elect Power Res Inst Guangxi Power Grid Equipment Monitoring & Diag En Nanning Peoples R China

ISBN: (纸本)9781665490542

It is critical to forecast the electric load for a region. Traditional electric load forecasting frequently predicts the load of multiple transformers in the region after directly summing them, but directly predicting the total load after accumulating the load of each distribution transformer will weaken the individual time-series characteristics and reduce prediction accuracy, so it is necessary to forecast the total load of the region while appropriately retaining the individual time-series characteristics. To address the aforementioned issues, this paper clusters transformer load curves, narrows the load characteristics of the same category of transformers using the weighting concept. It stores the load characteristics of multiple transformers in a neural network. This paper develops a virtual transformer load forecasting model in which the sum of all transformer loads in the region is a number of times the virtual transformer load forecasting result.

关键词： load forecasting k-means clustering algorithm long short-term memory

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Service quality evaluation of agricultural cold chain logistics supply chain based on k-means clustering algorithm

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International Journal of Manufacturing Technology and Management 2025年第1-2期39卷 59-73页

作者： Zhang, YuXi School of Economics and Trade Henan Polytechnic Institute Nanyang473000 China

In order to improve the reliability of the cold chain logistics supply chain and shorten the response time of the supply chain, a quality evaluation method of agricultural cold chain logistics supply chain based on k-means clustering algorithm was proposed. Firstly, build the quality evaluation system of agricultural cold chain logistics supply chain. Secondly, the root method is selected to check the consistency of the judgment matrix, and the weight vector of the logistics supply chain quality evaluation is calculated. Finally, the k-means clustering algorithm is used to evaluate the supply chain service quality. The experimental results show that the supply chain quality reliability of this method is 0.98, and the service response time is only six minutes;the service satisfaction rate can reach 99.6%. © 2025 Inderscience Enterprises Ltd.

关键词： agriculture products analytic hierarchy process cold chain logistics k-means clustering algorithm service quality assessment weight vector

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Digital Visual Design Reengineering and Application Based on k-means clustering algorithm

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2024年第4期11卷 1-13页

作者： Ren, Lijie kim, Hyunsuk Hongik Univ Seoul 100744 South Korea

INTRODUCTION: The article discusses the key steps in digital visual design reengineering, with a special emphasis on the importance of information decoding and feature extraction for flat cultural heritage. These processes not only minimize damage to the aesthetic heritage itself but also feature high quality, efficiency, and recyclability. OBJECTIVES: The aim of the article is to explore the issues of gene extraction methods in digital visual design reengineering, proposing a visual gene extraction method through an improved k-means clustering algorithm. METHODS: A visual gene extraction method based on an improved k-means clustering algorithm is proposed. Initially analyzing the digital visual design reengineering process, combined with a color extraction method using the improved JSO algorithm-based k-means clustering algorithm, a gene extraction and clustering method for digital visual design reengineering is proposed and validated through experiments. RESULT: The results show that the proposed method improves the accuracy, robustness, and real-time performance of clustering. Through comparative analysis with Dunhuang murals, the effectiveness of the color extraction method based on the k-means-JSO algorithm in the application of digital visual design reengineering is verified. The method based on the k-means-GWO algorithm performs best in terms of average clustering time and standard deviation. The optimization curve of color extraction based on the k-means-JSO algorithm converges faster and with better accuracy compared to the k-means-ABC, k-means-GWO, k-means-DE, k-means-CMAES, and k-means-WWCD algorithms. CONCLUSION: The color extraction method of the k-means clustering algorithm improved by the JSO algorithm proposed in this paper solves the problems of insufficient standardization in feature selection, lack of generalizationability, and inefficiency in visual gene extraction methods.

关键词： digital visual design reengineering jellyfish optimization algorithm k-means clustering algorithm color gene extraction

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Sports competition data analysis and strategy optimization using k-means clustering algorithm

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年第1期25卷 888-902页

作者： An, Ni Guangdong Pharmaceut Univ Dept Phys Educ 280 Outer Ring East Rd Guangzhou 510006 Guangdong Peoples R China

Sports competition data analysis and strategy optimization are important ways to enhance athlete competitiveness and team collaboration. The current competition analysis and strategy formulation have strong subjectivity, making it difficult to deeply understand the performance characteristics and patterns of athletes and teams. Traditional analysis methods cannot accurately identify the performance differences of different athletes, and there are limitations in their feature recognition and classification. In order to enhance the scientificity of strategy formulation and improve the performance of athletes in competitions, this article combined the k-means clustering algorithm and focused on basketball sports to conduct an in-depth analysis of sports competition data analysis and strategy optimization. Firstly, the competition data was collected and preprocessed. Then, feature selection was carried out from three dimensions: competition results, player performance, and team characteristics. Finally, the k-means clustering algorithm was used to perform hierarchical clustering on the original data through a hierarchical method. To verify its effectiveness, this article conducted practical analysis on the data of nearly 5 basketball competitions in 10 university basketball leagues in a certain province and optimized strategies based on cluster analysis. The results showed that in terms of player performance, compared to before optimization, the average number of rebounds, assists, and steals of team players optimized based on algorithm strategy increased by about 38.9%, 25.0%, and 63.2%, respectively. The conclusion indicates that the application of k-means clustering algorithm in sports competition data analysis and strategy optimization can help improve the competitive level of athletes and enhance their performance.

关键词： sports competition data analysis strategy optimization k-means clustering algorithm basketball performance

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Channeling analysis of wavelet threshold processing based on k-means clustering algorithm

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ACTA GEOPHYSICA 2023年第5期71卷 2137-2147页

作者： Gan, Lixiong Li, Ming Cai, Wenyuan Li, Jian Chen, Zhanglong Sun, Jian Deng, Rui Yangtze Univ Key Lab Oil & Gas Resources & Explorat Technol Minist Educ Wuhan 430100 Hubei Peoples R China PetroChina Qinghai Oilfield Co Well Testing Co Mangya 817500 Qinghai Peoples R China CNPC Logging Co Ltd Xian 710077 Shaanxi Peoples R China

Through the spectrum noise logging technology, the oil field is dynamically monitored, and according to its simple logging instrument and convenient operation, the position of the outer channeling of the casing can be qualitatively judged by the abnormal noise of the measurement record, and the downhole production status of the water injection well can be accurately diagnosed. Fully grasp the problems of oil casing leakage, outer channeling and packer leakage in water injection wells, and enrich downhole operations. In this paper, the downhole noise signal data are standardized, and the k-means clustering algorithm is used to classify the downhole noise signal according to the correlation coefficient of different frequencies to obtain the low-frequency noise signal, and the low-frequency noise signal is clustered twice to obtain the channeling frequency band and the reservoir fluid frequency band. The accurate channeling frequency range is determined and conforms to the domestic and foreign research data. The channeling frequency band is processed by wavelet threshold, and the useless noise in the channeling frequency band is eliminated. The channeling noise signal curve after processing is analyzed, and the main output layers have an obvious amplitude back channeling. The k-means clustering algorithm is used to analyze the channeling frequency band, and the channeling noise is processed by wavelet threshold. It is a new noise signal curve processing method, which provides a new idea for the spectrum noise logging technology to master the problem of channeling outside the pipe in the water injection well.

关键词： Spectrum noise logging Channeling k-means clustering algorithm Wavelet threshold processing Accuracy

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Optimizing High School English Textbook Content Structure Using the k-means clustering algorithm

Optimizing High School English Textbook Content Structure Us...

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International Conference on Informatics Education and Computer Technology Applications (IECA)

作者： Du, Yutong Soochow Univ Sch Foreign Language Suzhou 215006 Peoples R China

ISBN: (纸本)9798350375343;9798350375336

Traditional high school English textbooks often need to be revised to address the varied learning preferences of students, resulting in disparities in teaching efficacy. This study takes the 2020 Yilin Edition of the high school English textbook as an example for analysis due to its widespread use and adherence to contemporary educational standards. It aims to remedy these gaps by applying a cluster-based analytical method to reform textbook content, focusing on individual differences. Through a detailed analysis of language difficulty, content breadth, and stylistic differences, the cluster-based analytical method reveals hidden patterns in textbook content distribution that provide empirical support for adjusting textbook structure and allocating personalized content. The findings indicate that this method effectively identifies textbook imbalances while offering a scientific direction for optimizing course design. The research underscores the utility of data mining in education and propounds an advisable methodology for advancing high school English instruction.

关键词： High school English teaching k-means clustering algorithm Textbook optimization Data analysis

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Classification Model for Diabetes Mellitus Diagnosis based on k-means clustering algorithm Optimized with Bat algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第1期14卷 653-659页

作者： Anam, Syaiful Fitriah, Zuraidah Hidayat, Noor Maulana, Mochamad Hakim Akbar Assidiq Brawijaya Univ Math Dept Malang Indonesia Brawijaya Univ Math Dept Malang Indonesia

Diabetes mellitus is a disease characterized by abnormal glucose homeostasis resulting in an increase in blood sugar. According to data from the International Diabetes Federation (IDF), Indonesia ranks 7th out of 10 countries with the highest number of diabetes mellitus patients in the world. The prevalence of patients with diabetes mellitus in Indonesia reaches 11.3 percent or there are 10.7 million sufferers in 2019. Prevention, risk analysis and early diagnosis of diabetes mellitus are necessary to reduce the impact of diabetes mellitus and its complications. The clustering algorithm is one of methods that can be used to diagnose and analyze the risk of diabetes mellitus. The k-mean clustering algorithm is the most commonly used clustering algorithm because it is easy to implement and run, computation time is fast and easy to adapt. However, this method often gets to be stuck at the local optima. The problem of the k -means clustering algorithm can be solved by combining the k -means clustering algorithm with the global optimization algorithm. This algorithm has the ability to find the global optimum from many local optimums, does not require derivatives, is robust, easy to implement. The Bat algorithm (BA) is one of global optimization methods in swarm intelligence class. BA uses automated enlargement techniques into a solution and it's accompanied by a shift from exploration mode to local intensive exploitation. Based on the background that has been explained, this article proposes the development of a classification model for diagnosing diabetes mellitus based on the k-means clustering algorithm optimized with BA. The experimental results show that the k-means clustering optimized by BA has better performance than k-means clustering in all metrics evaluations, but the computational time of the k-means clustering optimized by BA is higher than k-means clustering.

关键词： -Diabetes mellitus disease diagnosis methods k-means clustering algorithm optimization bat algorithm

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k-means clustering algorithm for data distribution in cloud computing environment

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INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING 2021年第3期12卷 322-331页

作者： Pan, Hailan Lei, Yongmei Yin, Shi Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China Shanghai Polytech Univ Res Ctr Resource Recycling Sci & Engn Shanghai 201209 Peoples R China Shanghai Polytech Univ Sch Econ & Management Shanghai 201029 Peoples R China

This study analyses the data structure in cluster analysis. It is a clustering method that randomly selects a known number of points and then continues to expand. Through the comparative experiments on the clustering accuracy of different similarity matrices, the experimental analysis on the effectiveness of the model, the distribution of e-commerce data under cloud computing and the calculation time of different clustering algorithms, we can better understand the k-means clustering algorithm and the status of e-commerce in cloud computing environment. The experimental results show that if the appropriate similarity function is selected, the result of spectral clustering is usually not lower than that of simple k-means clustering. When the number of users reaches 4000, the list reading time of the k-means clustering algorithm is 3.15 s, while the other three algorithms consume more time.

关键词： cloud computing data distribution oriented k-means clustering algorithm e-commerce platform data structure data distribution

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Research on parallel association rule mining of big data based on an improved k-means clustering algorithm

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INTERNATIONAL JOURNAL OF AUTONOMOUS AND ADAPTIVE COMMUNICATIONS SYSTEMS 2023年第3期16卷 233-247页

作者： Hao, Li Wang, Tuanbu Guo, Chaoping Xijing Univ Coll Informat Engn Xian 710123 Peoples R China

In order to overcome the problems of time-consuming, low-precision and redundant rules in association rule mining of big data, a parallel association rule mining method based on an improved k-means clustering algorithm is proposed. Establish a data object criterion function and optimise k-means clustering algorithm. The improved k-means clustering algorithm is used to cluster big data and improve the efficiency of mining association rules. This paper introduces the matter-element theory of extension, combines matter-element theory and database, and constructs the matter-element relation database model of extension to realise the mining of parallel association rules in big data on the basis of extension. Redundant algorithms and equivalent transformations are used to eliminate redundant association rules. The experimental results show that the proposed method has high mining efficiency, high mining accuracy, and high rule association, which proves that the proposed method has better application performance.

关键词： k-means clustering algorithm association rules data mining redundancy algorithm equivalence transformation

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Optimization Method for Predictive Models Based on ARIMA Time Series and k-means clustering algorithm 36

Optimization Method for Predictive Models Based on ARIMA Tim...

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36th Chinese Control and Decision Conference (CCDC)

作者： Ban, Rui Deng, Yuanyuan Shenyang Jianzhu Univ Sch Comp Sci & Engn Shenyang 110168 Peoples R China

ISBN: (纸本)9798350387780;9798350387797

This paper proposes a method for optimizing prediction models based on the ARIMA time series and the k-means clustering algorithm to address the challenge of solving world puzzle games. The method aims to offer players a diverse range of challenges by anticipating and predicting imbalances in difficulty and content in advance. To achieve this, the paper first establishes an ARIMA time series model to fit the data and calculate the goodness of fit. Subsequently, employing keywords such as vowels, consonants, word composition, and usage, the k-means clustering algorithm is applied to compute the clustering center of the data and generate a clustering result graph. Finally, different regression models were established for the characteristics of various difficulty samples, and the words were digitally encoded. The least squares method was used for fitting. Faced with different attempts, a reasonable prediction interval was obtained based on the analysis results. Experimental results indicate that, in comparison to traditional algorithms, the proposed method demonstrates higher accuracy in predicting word attribute classifications. The findings of this research offer an effective approach for game developers to manage puzzle game difficulty, enabling better balancing of game challenges and enhancing the gaming experience and engagement for players.

关键词： ARIMA time series model k-means clustering algorithm linear regression least square method

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