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Analysis of athletes' stadium stress source based on improved layered k-means algorithm

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第4期39卷 5905-5914页

作者： Gong, Chen Northeast Elect Power Univ Sch Phys Educ Jilin 132012 Jilin Peoples R China

Most of the research on stressors is in the medical field, and there are few analysis of athletes' stressors, so it can not provide reference for the analysis of athletes' stressors. Based on this, this study combines machine learning algorithms to analyze the pressure source of athletes' stadium. In terms of data collection, it is mainly obtained through questionnaire survey and interview form, and it is used as experimental data after passing the test. In order to improve the performance of the algorithm, this paper combines the known k-means algorithm with the layering algorithm to form a new improved layered k-means algorithm. At the same time, this paper analyzes the performance of the improved hierarchical k-means algorithm through experimental comparison and compares the clustering results. In addition, the analysis system corresponding to the algorithm is constructed based on the actual situation, the algorithm is applied to practice, and the user preference model is constructed. Finally, this article helps athletes find stressors and find ways to reduce stressors through personalized recommendations. The research shows that the algorithm of this study is reliable and has certain practical effects and can provide theoretical reference for subsequent related research.

关键词： k-means algorithm athlete stress source machine learning

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Advances in numerical simulation with a clustering method based on k-means algorithm and Adams Bashforth scheme for fractional order laser chaotic system

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ALEXANDRIA ENGINEERING JOURNAL 2023年 75卷 165-179页

作者： Sinan, Muhammad Leng, Jinsong Shah, kamal Abdeljawad, Thabet Univ Elect Sci & Technol China Sch Math Sci Chengdu 611731 Peoples R China Prince Sultan Univ Dept Math & Sci POB 66833 Riyadh 11586 Saudi Arabia Univ Malakand Dept Math Dir L Khyber Pakhtunk Pakistan China Med Univ Dept Med Res Taichung 40402 Taiwan Kyung Hee Univ Dept Math 26 Kyungheedae Ro Seoul 02447 South Korea

In this research work, we present a mathematical analysis of a fractional sixth-order laser model of a resonant which is homogeneously extended three-level optically pumped. We use Caputo fractional order derivative in the proposed model. Our analysis includes an investigation of various chaotic behaviors under fractional order derivative and qualitative theory of the existence of the solution to the proposed model. For our required analysis of qualitative type, we use formal analysis tools. Further, numerical simulations are performed with a clustering method based on the k- means algorithm and Adams Bashforth scheme. With the help of the aforesaid scheme, we present different chaotic behavior corresponding to various values of fractional order. Finally, we give a comparison of the CPU time of the proposed method with that of the Rk4 *** 2023 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://***/ licenses/by-nc-nd/4.0/).

关键词： Qualitative theory Chaotic behavior k-means algorithm A clustering method

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A modified version of the k-means algorithm with a distance based on cluster symmetry

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2001年第6期23卷 674-680页

作者： Su, MS Chou, CH Natl Cent Univ Dept Comp Sci & Informat Engn Chungli 320 Taiwan Tamkang Univ Dept Elect Engn Tamsui 25137 Taiwan

In this paper, we propose a modified version of the k-means algorithm to cluster data. The proposed algorithm adopts a novel nonmetric distance measure based on the idea of "point symmetry." This kind of "point symmetry distance" can be applied in data clustering and human face detection. Several data sets are used to illustrate its effectiveness.

关键词： data clustering pattern recognition k-means algorithm face detection

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Recognition of Mobile Robot Navigation Path Based on k-means algorithm

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2020年第8期34卷

作者： Du, Qifei Wang, Dahu Sha, Lin Tianjin Polytech Univ Natl Demonstrat Ctr Expt Engn Training Educ Tianjin Peoples R China Henan Polytech Univ Sch Elect Engn & Automat Jiaozuo Henan Peoples R China Tianjin Polytech Univ Sch Elect Engn & Automat Tianjin Peoples R China

With the rapid development of computer technology and electronics industry, computer processing capability and image processing technology have been greatly improved, making robots based on computer processing and image processing have entered a new development in the field of navigation path recognition research. As an indispensable carrier for intelligent manufacturing and industrial development, robots are expanding their applications. The key to the successful execution of the mobile robot is to move according to the planned path and to avoid obstacles autonomously. These two points depend on the validity and accuracy of navigation path identification. At present, research on mobile robot navigation path recognition mainly uses visual navigation as the main method, which uses visual sensors to simulate human eye functions, obtains relevant information from external environment images, and processes them to realize related functions that the system needs to complete. The two major problems in visual navigation are poor recognition ability and insufficient ability to resist light source interference. The main purpose of this paper is to improve the recognition ability of mobile robot navigation path and the ability to resist light source interference. It mainly uses the k-means algorithm for visual navigation research. By simulating the acquired image and the selected color space, the results show that the average time taken to complete a path identification method is 152 ins. Under different illumination environments, the information extraction rate of mobile robot navigation path can reach 90%, and the effect of strong light on navigation path recognition is effectively reduced under strong illumination environment. The results show that the recognition of the visual navigation path of a mobile robot using the k-means algorithm is more precise than the conventional method, and it takes less time to better meet the timeliness requirements of mobile robots.

关键词： k-means algorithm mobile robot visual navigation path recognition

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Optimisation of k-means algorithm based on sample density canopy

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INTERNATIONAL JOURNAL OF AD HOC AND UBIQUITOUS COMPUTING 2021年第1-3期38卷 62-69页

作者： Shen, Guo-xin Jiang, Zhong-yun Shanghai Ocean Univ Informat Inst Shanghai 210306 Peoples R China Shanghai Jianqiao Univ Informat Technol Inst Shanghai 210306 Peoples R China

Since the random selection of the initial centroid and the artificial definition of the number of clusters affect the experimental results of k-means, therefore, this article uses sample density and canopy to optimise the k-means algorithm. This algorithm first calculates the sample density of each data, and selects the data point with the smallest density as the first cluster centroid;then combines the canopy algorithm to cluster the original sample data to obtain the number of clusters and each cluster centre. As initial parameter of the k-means finally combines the k-means algorithm to assemble the original samples, UCI dataset and self-built dataset were used to compare simulation experiments. The results show that the algorithm can make clustering results more accurate, run faster, and improve the stability of the algorithm.

关键词： clustering k-means algorithm density neighbourhood initial centroid

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VQ codebook design using modified k-means algorithm with feature classification and grouping based initialization

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MULTIMEDIA TOOLS AND APPLICATIONS 2018年第7期77卷 8495-8510页

作者： Wang, Lang Lu, Zhe-Ming Ma, Long-Hua Feng, Ya-Pei Zhejiang Univ Ningbo Inst Technol Sch Informat Sci & Engn Ningbo 315100 Zhejiang Peoples R China Zhejiang Univ Sch Aeronaut & Astronaut Room 105Teaching Bldg 5Yuquan Campus Hangzhou 310027 Peoples R China

Vector quantization (VQ) has been successfully used in data compression and feature extraction areas. Codebook design is the essential step of VQ. The k-means algorithm is a famous data clustering technique which is also an efficient codebook design scheme. The main disadvantages of k-means algorithm lie in that the initial cluster centroids greatly affect the convergence speed and the final clustering performance. In the past two decades, many codebook initialization techniques have been proposed. However, most of these techniques do not make full use of the features of the training vectors, and some techniques require high extra computational load. This paper presents an efficient and simple technique for the conventional k-means algorithm based on feature classification and grouping. Firstly, all training vectors are classified into sixteen categories based on a two-level classifier including an edge classifier and a contrast classifier. Then, the training vectors in each category are sorted based on their norm values and divided into groups. Each group has the same size, and the centroid vector of each group is calculated as an initial codeword. Experimental results show that, compared with several typical initialization techniques, our technique can obtain a better codebook along with a faster convergence speed in a shorter time.

关键词： Vector quantization Codebook design Initial codebook generation k-means algorithm Image compression

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Nonparametric k-means algorithm with applications in economic and functional data

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2022年第2期51卷 537-551页

作者： Feng, Zhangmei Zhang, Jiamin Shanghai Univ Finance & Econ Shanghai Peoples R China

Inspired by the well-known relationship between k-means algorithm and Expectation-Maximization (EM) algorithm for mixture models, we propose nonparametric k-means algorithm for estimation of nonparametric mixture of regressions and mixture of Gaussian processes. The proposed methods are illustrated by extensive numerical simulations, comparisons, and analysis of two real datasets. Simulation studies and applications demonstrate that our method is an effective and competitive procedure for modified EM algorithm in nonparametric mixture settings.

关键词： k-means algorithm mixture models nonparametric regression Gaussian processes EM algorithm

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Analysis of debt-paying ability of real estate enterprises based on fuzzy mathematics and k-means algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第5期37卷 6403-6414页

作者： Qiang, Qunli Anhui Jianzhu Univ Sch Econ & Management Hefei Anhui Peoples R China

Currently, there is a certain fluctuation in the real estate industry, so it is particularly important to analyze the solvency of real estate enterprises. In order to find a reliable model suitable for studying the difference in house prices, this study collects the research data through data collection, and uses the k-means clustering method to construct the corresponding model as a basic research in combination with the machine learning research method. At the same time, this paper compares the analysis effects of several common machine learning models and finds the advantages and disadvantages of these methods through mathematical statistics. In addition, combined with practice, this paper constructs a nonlinear generalized additive model, and based on machine learning technology, validates the validity of the model based on data analysis, the collected predictors. In view of the improvement of the solvency of real estate enterprises, diversified operation of real estate enterprises can maintain reasonable cash flow and make up for the defect of poor liquidity of real estate. Furthermore, this paper uses the stability method to find the optimal model. In addition, the generalized additive model effectively reveals the complex nonlinear relationship between continuous predictors and house prices. Through research, it can be seen that the nonlinear generalized additive model based on machine learning can play an important role in real estate industry forecasting and has certain theoretical reference significance for subsequent related research.

关键词： Real estate generalized additive model machine learning k-means algorithm

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Modified global k-means algorithm for minimum sum-of-squares clustering problems

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PATTERN RECOGNITION 2008年第10期41卷 3192-3199页

作者： Bagirov, Adil M. Univ Ballarat Ctr Informat & Appl Optimizat Sch Informat Technol & Math Sci Ballarat Vic 3353 Australia

k-means algorithm and its variations are known to be fast clustering algorithms. However, they are sensitive to the choice of starting points and inefficient for solving clustering problems in large data sets. Recently, a new version of the k-means algorithm, the global k-means algorithm has been developed. It is an incremental algorithm that dynamically adds one cluster center at a time and uses each data point as a candidate for the k-th cluster center. Results of numerical experiments show that the global k-means algorithm considerably outperforms the k-means algorithms. In this paper, a new version of the global k-means algorithm is proposed. A starting point for the k-th cluster center in this algorithm is computed by minimizing an auxiliary cluster function. Results of numerical experiments on 14 data sets demonstrate the superiority of the new algorithm, however, it requires more computational time than the global k-means algorithm. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： minimum sum-of-squares clustering nonsmooth optimization k-means algorithm global k-means algorithm

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A Hybrid Method Combining Improved k-means algorithm with BADA Model for Generating Nominal Flight Profiles

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Transactions of Nanjing University of Aeronautics and Astronautics 2016年第4期33卷 414-424页

作者： Tang Xinmin Gu Junwei Shen Zhiyuan Chen Ping Li Bo College of Civil Aviation Nanjing University of Aeronautics and Astronautics The 28th Research Institute of China Electronic Technology Group Corporation

A high-precision nominal flight profile,involving controllers′intentions is critical for 4Dtrajectory estimation in modern automatic air traffic control *** proposed a novel method to effectively improve the accuracy of the nominal flight profile,including the nominal altitude profile and the speed ***,considering the characteristics of trajectory data,we developed an improved k-means *** approach was to measure the similarity between different altitude profiles by integrating the space warp edit distance algorithm,thereby to acquire several fitted nominal flight altitude *** approach breaks the constraints of traditional k-means ***,to eliminate the influence of meteorological factors,we introduced historical gridded binary data to determine the en-route wind speed and temperature via inverse distance weighted ***,we facilitated the true airspeed determined by speed triangle relationships and the calibrated airspeed determined by aircraft data model to extract a more accurate nominal speed profile from each cluster,therefore we could describe the airspeed profiles above and below the airspeed transition altitude,*** experimental results showed that the proposed method could obtain a highly accurate nominal flight profile,which reflects the actual aircraft flight status.

关键词： air transportation flight profile k-means algorithm space warp edit distance(SWED)algorithm trajectory prediction

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