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2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Wu, Huijiao Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350309614

With the continuous development of science and technology, the amount of data generated in life is increasing, and big data technology has now penetrated into all walks of life. This paper focuses on the application of big data technology in urban rail transit. Firstly, the related concepts of big data technology and urban rail transit are introduced in detail. Secondly, it emphasizes the necessity of using big data technology in urban rail transit;Finally, this paper expounds the examples of the application of big data technology in urban rail transit, including key applications such as operation and maintenance, accessibility analysis and passenger flow monitoring. Taking passenger flow monitoring as an example, based on the passenger flow data of Tianjin big data technology, the stations are classified by k-means clustering algorithm. Big data technology can help urban rail transit improve its operational efficiency in many aspects, promote its intelligent development and accelerate the process of urbanization. Faced with the abundance and complexity of data in the era of science and technology, it will become an inevitable trend to assist management decision-making through big data technology. © 2023 IEEE.

关键词： Accessibility model Big data technology k-means clustering algorithm Passenger flow monitoring Urban rail transit

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Research on English Teaching Quality Evaluation based on Fuzzy Comprehensive Evaluation based on k-means clustering algorithm 3

Research on English Teaching Quality Evaluation based on Fuz...

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

作者： Zhou, Yun Yuxi Normal University of Yunnan Province Yunnan Province 653100 China

ISBN: (纸本)9798350319125

With the acceleration of globalization, English plays an increasingly important role in international communication. Therefore, the improvement of English teaching quality has become an important issue that educators pay attention to. In order to make a comprehensive, objective and scientific evaluation of English teaching, this study adopts k-means clustering fuzzy comprehensive evaluation method, and analyzes the data of teachers, students and teaching environment, so as to provide targeted suggestions for improvement of English teaching © 2023 IEEE.

关键词： /b13>fuzzy comprehensive evaluation English language teaching k-means clustering algorithm Quality evaluation

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k-means clustering algorithm for Large-scale Chinese Commodity Information Web Based on Hadoop

K-means Clustering Algorithm for Large-scale Chinese Commodi...

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第十四届分布式计算及其应用国际学术研讨会

作者： Geng Yushui Zhang Lishuo School of Information Qilu University of Technology

With the growing popularity of the network, product information filled in the many pages of the Internet, which you want to get the information you need on these pages tend to consider clustering information, and the current explosive growth of data so that the information mass storage condition occurs, clustering to facing the problems such as large calculation complexity and time consuming, then the traditional k-means clustering algorithm does not meet the needs of large data environments today, so this article combined with the advantages of the Hadoop platform and MapReduce programming model is proposed the k-means clustering algorithm for large-scale chinese commodity information Web based on Hadoop. Map function calculates the distance from the cluster center for each sample and mark to their category, Reduce function intermediate results are summarized and calculated new clustering center for the next round of iteration. Experimental results show that this method can better improve the clustering processing speed.

关键词： k-means clustering algorithm Hadoop platform MapReduce Cloud computing Big Data

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Research on Load clustering Based on Singular Value Decomposition and k-means clustering algorithm

Research on Load Clustering Based on Singular Value Decompos...

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2020 Asia Energy and Electrical Engineering Symposium

作者： Wang, Jiao Wang, kaiyan Jia, Rong Chen, Xiaofei Xian Univ Technol Sch Elect Engn Xian Peoples R China Shaanxi Reg Elect Power Co LTD Xianyang Elect Power Supply Xianyang Peoples R China

ISBN: (数字)9781728167824

ISBN: (纸本)9781728167824

Considering the shortcomings of existing clustering algorithms in clustering quality, this paper proposes a load clustering research based on singular value decomposition and k-means clustering algorithm. First, eight characteristic indexes of load are extracted, and the singular value is used to decompose the load characteristics of the user side. The solved singular value reflects the importance of this type of load characteristic. The load characteristics corresponding to the data with large singular value are taken as the main load characteristics to complete the dimensionality reduction of the data. Then, the clustering evaluation index SSE is used to compare the effects of direct clustering of load characteristics and clustering after singular value decomposition. The results show that the proposed method has better clustering effect on load characteristics. Finally, k-means clustering algorithm is used to cluster the load characteristics.

关键词： singular value decomposition k-means clustering algorithm load characteristic Dimensionality reduction of data cluster evaluation index

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Suitable error evaluation criteria selection in the wind energy assessment via the k-means clustering algorithm

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INTERNATIONAL JOURNAL OF GREEN ENERGY 2016年第11期13卷 1145-1162页

作者： Wu, Jie Wang, Jianzhou Qin, Shanshan Lu, Haiyan Northwest Univ Nationalities Sch Math & Comp Sci Lanzhou Peoples R China Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China Lanzhou Univ Res Sch Arid Environm & Climate Change MOE Key Lab Western Chinas Environm Syst Lanzhou Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW Australia

In this paper, wind energy potential of four locations in Xinjiang region is assessed. The Weibull distribution as well as the Logistic and the Lognormal distributions are applied to describe the distributions of the wind speed at different heights. In determining the parameters in the Weibull distribution, four intelligent parameter optimization approaches including the differential evolutionary, the particle swarm optimization, and two other approaches derived from these two algorithms and combined advantages of these two approaches are employed. Then the optimal distribution is chosen through the Chi-square error (CSE), the kolmogorov-Smirnov test error (kSE), and the root mean square error (RMSE) criteria. However, it is found that the variation range of some criteria is quite large, thus these criteria are analyzed and evaluated both from the anomalous values and by the k-means clustering method. Anomaly observation results have shown that the CSE is the first one should be considered to be eliminated from the consequent optimal distribution function selection. This idea is further confirmed by the k-means clustering algorithm, by which the CSE is clustered into a different group with kSE and RMSE. Therefore, only the reserved two error evaluation criteria are utilized to evaluate the wind power potential.

关键词： Anomalous value k-means clustering algorithm parameter optimization wind energy wind power classification

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Identification of new candidate drugs for lung cancer using chemical-chemical interactions, chemical-protein interactions and a k-means clustering algorithm

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JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS 2016年第4期34卷 906-917页

作者： Lu, Jing Chen, Lei Yin, Jun Huang, Tao Bi, Yi kong, Xiangyin Zheng, Mingyue Cai, Yu-Dong Yantai Univ Collaborat Innovat Ctr Adv Drug Delivery Syst & B Minist Educ Sch PharmKey Lab Mol Pharmacol & Drug Evaluat Yantai 264005 Peoples R China Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China Shanghai Jiao Tong Univ Sch Med SJTUSM Inst Hlth Sci Key Lab Stem Cell Biol Shanghai 200025 Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci Shanghai 200025 Peoples R China Shanghai Inst Mat Med Drug Discovery & Design Ctr Shanghai 201203 Peoples R China Shanghai Univ Coll Life Sci Shanghai 200444 Peoples R China

Lung cancer, characterized by uncontrolled cell growth in the lung tissue, is the leading cause of global cancer deaths. Until now, effective treatment of this disease is limited. Many synthetic compounds have emerged with the advancement of combinatorial chemistry. Identification of effective lung cancer candidate drug compounds among them is a great challenge. Thus, it is necessary to build effective computational methods that can assist us in selecting for potential lung cancer drug compounds. In this study, a computational method was proposed to tackle this problem. The chemical-chemical interactions and chemical-protein interactions were utilized to select candidate drug compounds that have close associations with approved lung cancer drugs and lung cancer-related genes. A permutation test and k-means clustering algorithm were employed to exclude candidate drugs with low possibilities to treat lung cancer. The final analysis suggests that the remaining drug compounds have potential anti-lung cancer activities and most of them have structural dissimilarity with approved drugs for lung cancer.

关键词： chemical-protein interaction k-means clustering algorithm lung cancer chemical-chemical interaction

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Comparison and Detection Analysis of Network Traffic Datasets Using k-means clustering algorithm

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JOURNAL OF INFORMATION & kNOWLEDGE MANAGEMENT 2020年第3期19卷

作者： Al-Sanjary, Omar Ismael Bin Roslan, Muhammad Aiman Helmi, Rabab Alayham Abbas Ahmed, Ahmed Abdullah Management & Sci Univ Fac Informat Sci & Engn Shah Alam 40100 Malaysia Qaiwan Int Univ QIU Fac Engn & Sci Sulaymaniyah Kurdistan Regio Iraq

Anomaly detection in specific datasets involves the detection of circumstances that are common in a homogeneous data. When looking at network traffic data, it is generally difficult to determine the type of attack without proper analysis and this holds true when simply viewing a record of network traffic with thousands of internet users to detect malicious activity. However, there are different types of datasets in light of the way they record or acquire data and facts. The paper aims to compare and analyse multiple datasets including NSL-kDD and MAWI by using k-means clustering algorithm. Specifically, the paper analyses the blind-Spots of the datasets and evaluates the most suitable dataset for k-means clustering algorithm. This paper's quantitative data analysis results are helpful in evaluating weaknesses of each dataset of traffic data, when using k-means clustering algorithm.

关键词： NSL-kDD MAWI k-means clustering algorithm

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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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Zoning of reservoir water temperature field based on k-means clustering algorithm

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JOURNAL OF HYDROLOGY-REGIONAL STUDIES 2022年 44卷

作者： Liu, Wei Zou, Peng Jiang, Dingguo Quan, Xiufeng Dai, Huichao China Three Gorges Univ Coll Hydraul & Environm Engn Yichang 443002 Peoples R China China Three Gorges Corp Beijing 100038 Peoples R China Hohai Univ Key Lab Coastal Disaster & Def Minist Educ Nanjing 210098 Peoples R China

Study region: Dongqing Reservoir located in Guizhou, China. Study focus: Zoning the RWTF (reservoir water temperature field) is of great significance and is an effective way to analyze RWTF's nature. In practice, the conditions of RWTF fluctuate greatly as time goes on, which leads to the existing RWTF zoning methods can't give a steady zoning result. In consequence, this paper creates a kind of zoning method to study the properties of RWTF in Dongqing Reservoir, which has two main steps: firstly, numerical simulation is used to obtain the whole data of RWTF, and then the k-means clustering algorithm is executed based on the numerical simulation results. New hydrological insights for the region: This paper proved that the zoning method developed in this paper, which combined numerical simulation and unsupervised machine learning, can be effectively applied and divided the RWTF into four zones without using experimental parameters in Dongqing Reservoir. Moreover, on the base of the four zones' spatial borders, the influencing factors of water temperature in each zone of Dongqing Reservoir were able to be found, which would be of value in further research of RWTF.

关键词： Reservoir water temperature field Unsupervised machine learning k-means clustering algorithm Numerical simulation Zoning method

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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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