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

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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The Application of Simulated Annealing k-means clustering algorithm in Combination Modeling 11

The Application of Simulated Annealing K-means Clustering Al...

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11th World Congress on Intelligent Control and Automation

作者： Dong Tao Ding Jian Yang Hui-zhong Lei Yu Tao Hongfeng Jiangian Univ Key Lab Adv Proc Control Light Ind Ministry Educ Wuxi 214122 Peoples R China Wontexpower Corp Wuxi 214125 Peoples R China

ISBN: (纸本)9781479958252

Traditional k-means clustering algorithm easily fall into local extremum. A maximum distances product algorithm is used to optimize the initial clustering centers and a k-means clustering algorithm with simulated annealing (SA) is promoted. The proposed method uses SA to optimize the clustering pattern in clustering analysis which can achieve global optimization. A combination model based on support vector machine (SVM) is established. The method is applied to a soft sensor modeling for the quality index in a Bisphenol A production process. The simulation result shows that the change trend of phenol content is tracked effectively and data classification result is improved by the algorithm. It also shows that the estimation precision of the soft sensor model is improved which demonstrates the potential application in industry field.

关键词： k-means clustering algorithm Initial Cluster Centers Simulated Annealing Combination SVM

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Integrating k-means clustering algorithm for the symbiotic relationship of aesthetic community spatial science

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JOURNAL OF INTELLIGENT SYSTEMS 2023年第1期32卷

作者： Bao, Yuanyang Yu, Qianqian Guangdong Univ Technol Sch Art & Design Guangzhou 510090 Peoples R China Guangdong Univ Technol Sch Art & Design Guangzhou 510090 Peoples R China

In modern society, the scientific symbiotic relationship of aesthetic community space is crucial for the development of cities. This article aimed to explore the symbiotic relationship between aesthetic community space and science, and analyze this relationship based on the k-means clustering algorithm. The article analyzed the spatial characteristics of different types of aesthetic communities, as well as the commonalities and differences between aesthetic communities and science, including the complementary relationship between the two. The k-means clustering algorithm is integrated to classify community spatial features, thereby analyzing and optimizing the relationship between the two. The experiment selected a sample of an aesthetic community and divided it into three different types of spatial features: architectural area, landscape greening area, and other activity areas. This article also compared the clustering results of the k-means algorithm with the k-means algorithm based on three-dimensional grid space. The results showed that the k-means algorithm optimized by the principle of three-dimensional grid space had stronger clustering performance. In the experiment, four different types of clustering algorithms and multiple evaluation indicators were also selected for testing. The data showed that in the clustering of spatial features in building areas, the k-means average clustering error score was controlled at around 13-14 under different missing ratios;the minimum average clustering error score of the characteristic data of the landscape greening area was 12.1. The average clustering error score in spatial feature clustering of other activity areas reached a minimum value of 23.5 when the missing ratio was 10%, which was lower than other algorithms. The overall experimental results indicate that the fused k-means clustering algorithm can effectively partition the aesthetic community space and optimize scientific symbiotic relationships.

关键词： aesthetic community scientific symbiosis k-means clustering algorithm data mining spatial analysis

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A k-means clustering algorithm for Automatically Obtaining k Value

A K-means Clustering Algorithm for Automatically Obtaining K...

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2018 3rd International Conference on Electrical, Control and Automation Engineering (ECAE 2018)

作者： Yuan-qiang XIE Rui-ming FANG College of Information Science and Engineering Huaqiao University

In order to solve the problem of manual input of k value in traditional k-means clustering algorithm, a method to obtain k value automatically is proposed. Firstly, the data needed to be clustered are sampled, and the distance between the data in the sample group is calculated, Then the distance matrix is formed and the de-noising process is done, and the clustering number k is selected based on the principle of distance maximization. Finally, we take two-dimensional data as an example to simulate the k-means clustering algorithm of the number of clusters k based on the principle of distance maximization, then the simulation results are verified by MATLAB. The experimental results show that the algorithm can obtain k value automatically and improve the accuracy of clustering.

关键词： k-means clustering algorithm Distance matrix Distance maximization Automatic acquisition

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An improved k-means clustering algorithm based on the optimization of initial centroids and iterations

An improved k-means clustering algorithm based on the optimi...

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2011 3rd IEEE International Conference on Information Management and Engineering(ICIME 2011)

作者： Li Lei Pattern Recognition and Intelligence System, College of Automation, Nanjing University of Post and Telecommunications Nanjing, China Hang Xin Pattern Recognition and Intelligence System, College of Automation, Nanjing University of Post and Telecommunications Nanjing, China

This paper presents a new method to preprocess the datasets, and an efficient way for assigning data points to clusters. The improved method avoids k-means algorithm sensitive to the initial centroids and instability, and it avoids the large number of iterations, saving the running time. Experimental results show that the improved method can effectively improve the speed of clustering and accuracy, reducing the computational complexity of the k-means.

关键词： k-means clustering algorithm initial centroids iterations computational complexity data mining

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Researches on the Calculation Method of Saturation Flow Rate at Signalized Intersection Based on k-means clustering algorithm

Researches on the Calculation Method of Saturation Flow Rate...

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International Conference on Computer Technology, Electronics and Communication

作者： Weiming Luo Jianhua Yuan Qing Ma Lin Xu Traffic Management Research Institute of Ministry of Public Security Ministry Wuxi Jiangsu China

ISBN: (纸本)9781538657850

This paper studies k-means clustering algorithm for the calculation of saturation flow rate at signalized intersection. The calculation results are based on the measured traffic data, which are surveyed at the intersection of Xinhua Road and Youyi Road in Tangshan. By analyzing the distribution characteristics of average headway in a day, this paper presents the negative influences of unsaturated or oversaturated traffic flow on headway. A portion of the average headway which affected by unsaturated or oversaturated condition are removed by k-means clustering algorithm. The developed method is used to calculate the saturation flow rate within different conditions. The calculation results illustrate that the proposed method can offset the effects of unsaturated or oversaturated condition.

关键词： The saturation flow rate Headway Signalized intersection k-means clustering algorithm

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An Enhanced Regularized k-means Type clustering algorithm With Adaptive Weights

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IEEE ACCESS 2020年 8卷 31171-31179页

作者： Wu, Ziheng Wu, Zixiang Anhui Univ Technol Sch Elect & Informat Engn Maanshan 243000 Peoples R China Zhejiang Univ Sch Med Affiliated Hosp 2 Dept Thorac Surg Hangzhou 310000 Peoples R China

$k$ -means clustering algorithm is one of the most popular technique for clustering in machine learning, however, in the existing $k$ -means clustering algorithm, the ability of the different features and the importance of the different data objects are treated equally;the discriminative ability of the different features and the importance of the different data objects cannot be differentiated effectively. In the light of this limitation, this paper put forward an enhanced regularized $k$ -means type clustering algorithm with adaptive weights in which we introduced an adaptive feature weights matrix and an adaptive data weights vector into the objective function of the $k$ -means clustering algorithm and we developed a new objective function with $l<^>clustering$ -norm regularization to the weights of data objects and features, then we obtained the corresponding scientific updating iterative rules of the weights of the different features, the weights of the different data objects and the cluster centers theoretically. In order to evaluate the performance of the new algorithm put forward, extensive experiments were conducted. Experimental results have indicated that our proposed algorithm can improve the clustering performance significantly and are more effective with respects to three metrics: the successful clustering rate (SCR), normal mutual information (NMI) and RandIndex.

关键词： k-means clustering algorithm machine learning adaptive weights l(2)-norm regularization

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Optimal Sensor Placement Based on Bisect k-means clustering algorithm

Optimal Sensor Placement Based on Bisect K-means Clustering ...

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2018 3rd International Conference on Materials Science,Machinery and Energy Engineering(MSMEE 2018)

作者： Zhaolan Wei Jing Xia College of Civil Engineering Sichuan Agricultural University

Sensor placement is a combinatorial optimization problem. Considering the number of factors, the selection of measuring points is easy to cause information redundancy and low signal-to-noise ratio. In order to solve this problem, according to the matrix of structure’s frequency response, bisect k-means clustering algorithm is designed to classify the degrees of freedom according to the similarity of response. This method is applied to the steel truss arch bridge with the background of Nanjing Dashengguan Yangtze River Bridge. The results show that the proposed method in this paper can better classify the degrees of freedom with similar vibration characteristics, make the sensor more balanced in the overall structure, overcome the redundant information among the sensors, and improve the signal-to-noise ratio at the measurement point.

关键词： Sensor k-means clustering algorithm measurement point

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Application of k-means clustering algorithm in human resource data informatization 2020

Application of K-means clustering algorithm in human resourc...

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Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies

作者： Yang Zhao Business management and human resources Wuhan Business University Wuhan China

ISBN: (纸本)9781450387828

Information based human resource management is a complete solution to enterprise human resource management through information technology. It is a new human resource management mode based on advanced software and high-speed and large-capacity hardware. It can reduce costs, improve efficiency and improve employees through centralized information base, automatic processing of information, self-service, outsourcing and service sharing. The purpose of the service model. It is connected with the existing network technology of enterprises and institutions to ensure the synchronous development of the technological environment with the rapid development of human resources. Human resource management is to optimize the allocation of human resource management in a planned way according to the development needs of the organization. Through the recruitment, training, assessment, incentive and other aspects of the staff, the enthusiasm of the staff can be brought into play, so as to create maximum benefits for the organization. Human resources are the most important resources in today's society. How to optimize the allocation and efficient use of human resources management departments should fully consider the problem. In today's data and information technology developed today, enterprises and institutions should use the Internet, big data and other means to scientifically transform, optimize and innovate the human resources management of institutions, and expand more new paths.

关键词： informatization data human resources k-means clustering algorithm

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