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作者： Ye Xiong Qingyu Peng Zhenhang Zhang School of Automation & Electrical Engineering Lanzhou Jiaotong University Construction Second Division CMCU Engineering Co. Ltd. School of Electrical Engineering Chongqing University

The traditional k-means clustering algorithm occupies a large quantity of memory resources and computing costs when dealing with massive data. It is easy to be restricted by something such as the initial center point as well abnormal data, and usually can not achieve effective clustering of large-scale data. In order to effectively solve the limitations of the algorithm, we propose a MapReduce parallel optimization method based on improved k-means clustering algorithm. Firstly, differential evolution theory is introduced to determine the optimal initial clustering center, after that, on the basis of the influence of samples on clustering results, the corresponding weighted Euclidean distance is designed to achieve effective data differentiation, so as to effectively reduce the impact of samples on clustering *** negative effect of abnormal data on clustering analysis can improve the accuracy of clustering. Finally, MapReduce programming model is used to realize parallel clustering. We use UCI datasets to verify the parallel optimization method. From the experimental results we can clearly know that the method we proposed has relatively stable parallel clustering results, faster operation speed, and effectively saves the operation time.

关键词： k-means clustering algorithm MapReduce programming model Differential evolution algorithm UCI dataset Initial clustering center Weighted Euclidean distance

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A Credit Rating Model for Enterprises Based on kernel Density Estimation and k-means clustering algorithm

A Credit Rating Model for Enterprises Based on Kernel Densit...

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工程和商业管理国际学术会议

作者： Zhang Mu 1,2 , Zhou Zong-fang 1 1. School of Management and Economics, University of Electronic Science and Technology of China, UESTC, Chengdu, China 2. School of Finance, Guizhou College of Finance and Economics, GZCFE, Guiyang, China

This paper proposes a new credit rating model for enterprises based on kernel density estimation and k-means clustering algorithm. Firstly, using the principal component analysis, the comprehensive credit score of each sample is obtained. Secondly, the kernel density estimation method is applied to analyze the comprehensive credit score series, and then the initial cluster centers are determined according to the local maximum points of density function. Finally, using k-means clustering algorithm, the final cluster centers are obtained, and the credit grades are divided. Thus, the credit rating for enterprises is realized. Through a specific example, it is proved that the model proposed by this paper is feasible and effective.

关键词： credit rating principal component analysis kernel density estimation initial cluster centers k-means clustering algorithm

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Identification of column edges of DNA fragments by using k-means clustering and mean algorithm on lane histograms of DNA agarose gel electrophoresis images 7

Identification of column edges of DNA fragments by using K-m...

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7th International Conference on Digital Image Processing (ICDIP)

作者： Turan, Muhammed kamil Sehirli, Eftal Elen, Abdullah karas, Ismail Rakip Karabuk Univ Karabuk Turkey

ISBN: (纸本)9781628418293

Gel electrophoresis (GE) is one of the most used method to separate DNA, RNA, protein molecules according to size, weight and quantity parameters in many areas such as genetics, molecular biology, biochemistry, microbiology. The main way to separate each molecule is to find borders of each molecule fragment. This paper presents a software application that show columns edges of DNA fragments in 3 steps. In the first step the application obtains lane histograms of agarose gel electrophoresis images by doing projection based on x-axis. In the second step, it utilizes k-means clustering algorithm to classify point values of lane histogram such as left side values, right side values and undesired values. In the third step, column edges of DNA fragments is shown by using mean algorithm and mathematical processes to separate DNA fragments from the background in a fully automated way. In addition to this, the application presents locations of DNA fragments and how many DNA fragments exist on images captured by a scientific camera.

关键词： Lane histogram k-means clustering algorithm mean algorithm DNA fragments

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Construction of aesthetic education evaluation model based on k-means clustering analysis in the context of artificial intelligence

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年第1期25卷 728-743页

作者： Li, Mo Guo, Shasha Chen, Jie Hebei Univ Engn Student Affairs Off Handan Peoples R China Hebei Univ Engn Sch Earth Sci & Engn 19Taiji RdEcon Devel Zone Handan 056038 Hebei Province Peoples R China

Enhancing aesthetic education can boost individuals' skills in recognizing, understanding, appreciating, and creating beauty. Nevertheless, traditional methods for evaluating aesthetic education are often subjective and have limited capacity to handle complex features effectively. This article was based on artificial intelligence to develop an aesthetic education evaluation model, and used an improved k-means clustering algorithm and a pre-trained ResNet-101 model for feature extraction. This article used WikiArt as an experimental data set, which contains 77,229 artworks from 15 art styles. This study used silhouette coefficient, Davies-Bouldin index, Calinski-Harabasz index, and Dunn index to evaluate the model clustering effect. Experimental results indicated that with a clustering \(k\) value of 10, the enhanced algorithm significantly advanced clustering quality, computational efficiency, and memory usage. This model has enhanced the objectivity and efficiency of aesthetic education evaluation and introduced new approaches for classifying and assessing artistic works.

关键词： k-means clustering algorithm aesthetic education evaluation artificial intelligence feature extraction ResNet-101 model

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Customer Segmentation Using k-means clustering and the Hybrid Particle Swarm Optimization algorithm

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COMPUTER JOURNAL 2023年第4期66卷 941-962页

作者： Li, Yue Qi, Jianfang Chu, Xiaoquan Mu, Weisong China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Minist Agr Key Lab Viticulture & Enol Beijing 100083 Peoples R China

In a competitive market, it is of great significance to divide customer groups to develop customer-centered personalized products. In this paper, we propose a customer segmentation method based on the k-means algorithm and the improved particle swarm optimization (PSO) algorithm. As the PSO algorithm easily falls into local extremum, the improved hybrid particle swarm optimization (IHPSO) algorithm is proposed to improve optimization accuracy. The full factorial design is used to determine the optimal parameter combination;the roulette operator is used to select excellent particles;then, the selected particles are crossed according to their adaptive crossover probabilities;when the population falls into a local optimum, the particles are mutated according to their adaptive mutation probabilities. Aimed at the k-means' sensitivity to selecting the initial cluster centers, IHPSO is used to optimize the cluster centers (IHPSO-kM). We compare IHPSO with the PSO, LDWPSO, GA, GA-PSO and ALPSO algorithms on nine benchmark functions. We also conduct comparative experiments to compare IHPSO-kM with several conventional and state-of-the-art approaches on five UCI datasets. All results show that the two proposed methods outperform existing models. Finally, IHPSO-kM is applied in customer segmentation. The experimental results also prove the rationality and applicability of IHPSO-kM for customer segmentation.

关键词： k-means clustering algorithm particle swarm optimization algorithm hybrid mechanism cluster analysis customer segmentation

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A k-means clustering-Based Multiple Importance Sampling algorithm for Integral Global Optimization

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Journal of the Operations Research Society of China 2023年第1期11卷 157-175页

作者： Chen Wang Dong-Hua Wu Department of Mathematics Shanghai UniversityShanghai200444China

In this paper, we propose a k-means clustering-based integral level-value estimation algorithm to solve a kind of box-constrained global optimization problem. For this purpose, we introduce the generalized variance function associated with the level-value of the objective function to be minimized. The variance function has a good property when Newton’s method is used to solve a variance equation resulting by setting the variance function to zero. We prove that the largest root of the variance equation is equal to the global minimum value of the corresponding optimization problem. Based on the k-means clustering algorithm, the multiple importance sampling technique is proposed in the implementable algorithm. The main idea of the cross-entropy method is used to update the parameters of sampling density function. The asymptotic convergence of the algorithm is proved, and the validity of the algorithm is verified by numerical experiments.

关键词： Global optimization Generalized variance function Multiple importance sampling k-means clustering algorithm

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Research on Grounding Grid Corrosion Diagnosis Based on Genetic k-means algorithm

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ENERGY SCIENCE & ENGINEERING 2025年

作者： Huang, Longsheng Xiao, Xianghui Huang, Mingxian Zhang, Zhenshan Song, Yunhao Guan, Luchang Foshan Univ Sch Mechatron Engn & Automat Foshan Peoples R China Foshan Univ Guangdong Prov Key Lab Ind Intelligent Inspect Tec Foshan Peoples R China

Grounding grid corrosion is one of the main reasons that affect the stable operation of electrical equipment in substations and endanger personal safety. After many years of operation, the grounding conductors will be eroded by soil. It may even cause major accidents and economic losses. Therefore, it is of great significance to diagnose the corrosion faults of the grounding grid and find out the corroded conductors. In this paper, the genetic k-means algorithm (GkA) is proposed to solve the mathematical model and judge the corrosion of grounding conductors. This algorithm combines GA's global searching ability and k-means's local searching ability, which improves the diagnosis result. In the simulation experiment, compared with the single GA's diagnosis, the diagnosis results of GkA were improved, and the number of misdiagnosed branches decreased by 66.7%. The simulation results show that the proposed algorithm takes less time to run, can eliminate the misdiagnosed branches commendably, and improve the accuracy of diagnosis. The proposed method provides a new idea to evaluate the corrosion degree of the grounding grid. The clustering algorithm is used to classify branches with similar corrosion degrees to achieve the purpose of corrosion diagnosis.

关键词： corrosion diagnosis electrical network theory genetic algorithm grounding grid k-means clustering algorithm

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Mapping global human thermal comfort: Spatiotemporal variability of UTCI regions under climate change

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SUSTAINABLE CITIES AND SOCIETY 2025年 119卷

作者： Zheng, Jing Du, Yaxing Zhu, Xian Dong, Wenjie Sun Yat Sen Univ Sch Atmospher Sci Key Lab Trop Atmosphere Ocean Syst Southern Marine Sci & Engn Guangdong Lab ZhuhaiMi Zhuhai 519082 Peoples R China

Human Thermal Comfort (HTC) is directly related to human health, quality of life, and sustainable urban planning. However, the global evaluation of outdoor thermal comfort via Universal Thermal Climate Index (UTCI) is still inadequate. This study systematically analyzed global HTC, via UTCI between 1951 and 2100 based on the Coupled Model Intercomparison Project Phase 6, covering historical trends and four Shared Socioeconomic Pathways (SSPs) future scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). The following key findings were presented: (1) The global average UTCI exhibits a latitudinal gradient distribution and a clear, consistent increase over time. (2) Ten characteristic thermal regions were identified by combining Empirical Orthogonal Function (EOF) and kmeans clustering. (3) EOF analysis revealed significant global warming and complex regional contrasts. (4) Low-latitude thermal regions expand poleward in all scenarios, with SSP5-8.5 causing major thermal property shifts and region redistributions. These results underscore a significant impact of changes inhuman thermal comfort under climate change, offering recommendations for interventions and policy guidance to address the challenges of climate change.

关键词： Human thermal comfort (HTC) Universal Thermal Climate Index (UTCI) k-means clustering algorithm Empirical orthogonal function (EOF) analysis

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Convex Optimization and PV Inverter Control Strategy-Based Research on Active Distribution Networks

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ENERGIES 2025年第7期18卷 1793-1793页

作者： Shi, Jiachuan Hu, Sining Fu, Rao Zhang, Quan Shandong Jianzhu Univ Sch Informat & Elect Engn Shandong Key Lab Smart Bldg & Energy Efficiency Jinan 250101 Peoples R China Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan 250101 Peoples R China

Optimizing the operation of active distribution networks (ADNs) has become more challenging because of the uncertainty created by the high penetration level of distributed photovoltaic (PV). From the convex optimization perspective, this paper proposes a two-layer optimization model to simplify the solution of the ADN optimal operation problem. Firstly, to pick out the ADN "key" nodes, a "key" nodes selection approach that used improved k-means clustering algorithm and two indexes (integrated voltage sensitivity and reactive power-balance degree) is introduced. Then, a two-layer ADN optimization model is built using various time scales. The upper layer is a long-time-scale model with on-load tap-changer transformer (OLTC) and capacitor bank (CB), and the lower layer is a short-time-scale optimization model with PV inverters and distributed energy storages (ESs). To take into account the PV users' interests, maximizing PV active power output is added to the objective. Afterwards, under the application of the second-order cone programming (SOCP) power-flow model, a linearization method of OLTC model and its tap change frequency constraints are proposed. The linear OLTC model, together with the linear models of the other equipment, constructs a mixed-integer second-order cone convex optimization (MISOCP) model. Finally, the effectiveness of the proposed method is verified by solving the IEEE33 node system using the CPLEX solver.

关键词： active distribution networks (ADN) convex optimization k-means clustering algorithm PV inverter second-order cone power flow (SOCP)

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GPR Diffraction Separation by Incorporating Multilevel Wavelet Transform and Multiple Singular Spectrum Analysis

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APPLIED SCIENCES-BASEL 2025年第6期15卷 3204-3204页

作者： Wang, Haolin Wang, Honghua Hou, Zhiyang Zhou, Fei Guilin Univ Technol Coll Earth Sci Guilin 541004 Peoples R China

By leveraging amplitude differences between reflected and diffracted signals in Ground Penetrating Radar (GPR) data, multiple singular spectrum analysis (MSSA) is considered an attractive approach to separate diffraction, which has identified great potential in their detectability of small-scale geological structures. However, conventional MSSA encounters difficulties in pinpointing the singular value threshold that corresponds to reflection, diffraction, and noise within the singular spectrum, leading to a resolution loss of the extracted diffraction profile. To address this issue, this paper develops a new technique that incorporates multilevel wavelet transform (MWT) and MSSA to separate GPR diffraction. By first implementing the MWT on GPR data decompose, the strategy can obtain various approximate detailed coefficients of multiple transformation levels for the subsequent inverse MWT to construct the corresponding coefficient profile. The issue of coefficient profiles that depict reflections often contains residual diffractions is also addressed by performing multiple singular spectrum SVDs based on the Hankel matrix within the dominant frequency domain. Building upon this, the k-means clustering algorithm is introduced to perform MSSA for classifying singular values into k categories. The diffraction wavefield is rebuilt by combining these outcomes with the coefficient profiles that depict diffractions at various transformation levels. Numerical tests showcase that the biorthogonal wavelet basis function bior4.4 provides remarkably efficient GPR diffraction separation performance, and the number of clusters in the k-means clustering algorithm typically ranges from 9 to 15, accounting for the complexity of the wave components. Compared to plane wave deconstruction (PWD), the proposed MWT-MSSA approach reduces energy loss at the diffraction vertex, decreases residual diffraction energy within the reflection profile, and enhances computational efficiency by approx

关键词： ground penetrating radar (GPR) multilevel wavelet transform (MWT) multiple singular spectrum analysis (MSSA) k-means clustering algorithm diffraction separation

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