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A deflation-adjusted Bayesian information criterion for selecting the number of clusters in k-means clustering

COMPUTATIONAL STATISTICS & DATA ANALYSIS 2025年 209卷

作者： Ueki, Masao Nagasaki Univ Sch Informat & Data Sci 1-14 Bunkyo Machi Nagasaki 8528521 Japan RIKEN Ctr Adv Intelligence Project Pathol Informat Team Nihonbashi 1 Chome Mitsui Bldg 15F1-4-1 Nihonbash Tokyo 1030027 Japan

A deflation-adjusted Bayesian information criterion is proposed by introducing a closed-form adjustment to the variance estimate after k-means clustering. An expected lower bound of the deflation in the variance estimate after k-means clustering is derived and used as an adjustment factor for the variance estimates. The deflation-adjusted variance estimates are applied to the Bayesian information criterion under the Gaussian model for selecting the number of clusters. The closed-form expression makes the proposed deflation-adjusted Bayesian information criterion computationally efficient. Simulation studies show that the deflation-adjusted Bayesian information criterion performs better than other existing clustering methods in some situations, including k-means clustering with the number of clusters selected by standard Bayesian information criteria, the gap statistic, the average silhouette score, the prediction strength, and clustering using a Gaussian mixture model with the Bayesian information criterion. The proposed method is illustrated through a real data application for clustering human genomic data from the 1000 Genomes Project.

关键词： Bayesian information criterion Deflation-adjustment to variance estimate k-means clustering

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Evapotranspiration Prediction Method Based on k-means clustering and QPSO-MkELM Model

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APPLIED SCIENCES-BASEL 2025年第7期15卷 3530-3530页

作者： Zhang, Chuansheng Yang, Minglai Shanghai Inst Technol Sch Railway Transportat Shanghai 201418 Peoples R China Jilin Agr Univ Coll Informat Technol Changchun 130118 Peoples R China

This study aims to improve the prediction accuracy of reference evapotranspiration under limited meteorological factors. Based on the commonly recommended PSO-ELM model for ET0 prediction and addressing its limitations, an improved QPSO algorithm and multiple kernel functions are introduced. Additionally, a novel evapotranspiration prediction model, kmeans-QPSO-MkELM, is proposed, incorporating k-means clustering to estimate the daily evapotranspiration in Yancheng, Jiangsu Province, China. In the input selection process, based on the variance and correlation coefficients of various meteorological factors, eight input models are proposed, attempting to incorporate the sine and cosine values of the date. The new model is then subjected to ablation and comparison experiments. Ablation experiment results show that introducing k-means clustering improves the model's running speed, while the improved QPSO algorithm and the introduction of multiple kernel functions enhance the model's accuracy. The improvement brought by introducing multiple kernel functions was especially significant when wind speed was included. Comparison experiment results indicate that the new model's prediction accuracy is significantly higher than all other comparison models, especially after including date sine and cosine values in the input. The new model's running speed is only slower than the RF model. Therefore, the kmeans-QPSO-MkELM model, using date sine and cosine values as inputs, provides a fast and accurate new approach for predicting evapotranspiration.

关键词： evapotranspiration machine learning prediction models Quantum Particle Swarm Optimization multi-kernel extreme learning machine k-means clustering

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Quantitative Comparison of Geographical Color of Traditional Village Architectural Heritage Based on k-means Color clustering-A Case Study of Southeastern Hubei Province, China

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BUILDINGS 2025年第5期15卷 748-748页

作者： Dong, Li kang, Meiqi Wuhan Univ Sci & Technol Coll Art & Design Wuhan 430065 Peoples R China

The architectural heritage of traditional villages, as an important bearing entity of regional culture, contains strong regional color attributes. However, under the wave of contemporary rapid economic development, the color of traditional village architectural heritage is facing serious challenges. The k-means clustering algorithm has outstanding advantages in image color clustering and is suitable for the large-scale data collection of sample picture primary colors to reduce subjective bias and can be combined with the HSV color space to optimize the results. In this study, the architectural heritage of four traditional villages of the Ming and Qing dynasties in the southeastern region of Hubei Province is taken as the research object, the k-means clustering algorithm is used to quantify the color data of the architectural heritage, and the HSV color space is used to analyze the distribution characteristics of the color data and to excavate the uniqueness of its colors and the regional characteristics. The results of this study show that the color characteristics of the architectural heritage of the four villages are as follows: the main colors, red-yellow and red, and the overall color percentage should be between 80% and 100%. The auxiliary colors, cyan blue and blue, should range from 0 to 20% and show low saturation and medium-high value characteristics. Based on the above results, the recommended range of values for the architectural heritage colors in the southeastern part of Hubei Province is clarified: the hue values are between the ranges of 0-40 and 200-230, the saturation is between 0 and 30%, and the values are in the range of 30-70%. At the same time, based on this range of values, a set of recommended chromatograms was generated to provide a visual reference for the adjustment of architectural heritage colors, which is helpful for the conservation and development of architectural heritage colors and landscapes.

关键词： traditional villages architectural heritage k-means clustering HSV color space color analysis color control

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k-means clustering for persistent homology

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ADVANCES IN DATA ANALYSIS AND CLASSIFICATION 2025年第1期19卷 95-119页

作者： Cao, Yueqi Leung, Prudence Monod, Anthea Imperial Coll London Dept Math South Kensington Campus London SW7 2AZ England

Persistent homology is a methodology central to topological data analysis that extracts and summarizes the topological features within a dataset as a persistence diagram. It has recently gained much popularity from its myriad successful applications to many domains, however, its algebraic construction induces a metric space of persistence diagrams with a highly complex geometry. In this paper, we prove convergence of the k-means clustering algorithm on persistence diagram space and establish theoretical properties of the solution to the optimization problem in the karush-kuhn-Tucker framework. Additionally, we perform numerical experiments on both simulated and real data of various representations of persistent homology, including embeddings of persistence diagrams as well as diagrams themselves and their generalizations as persistence measures. We find that k-means clustering performance directly on persistence diagrams and measures outperform their vectorized representations.

关键词： Alexandrov geometry karush-kuhn-Tucker optimization k-means clustering Persistence diagrams Persistent homology

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PTCTV-kMC: Infrared Small Target Detection Using Joint Partial Tensor Correlated Total Variation and k-means clustering

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 1252-1271页

作者： Huang, Zixu Zhao, Erwei Zheng, Wei Wen, Yan Peng, Xiaodong Niu, Wenlong Yang, Zhen Chinese Acad Sci Natl Space Sci Ctr Key Lab Elect & Informat Technol Space Syst Beijing 100190 Peoples R China Beijing Inst Remote Sensing Equipment Beijing 100854 Peoples R China

Infrared (IR) small target detection is widely utilized in both military and civilian sectors. Despite the development of numerous advanced methods for detecting small targets, improving overall performance in complex scenes remains a significant challenge. To address this issue, we propose a joint partial tensor correlated total variation and k-means clustering (PTCTV-kMC) method that integrates local and global features. The proposed method comprises two stages: seed point (i.e., candidate target) search and seed point discrimination. In the seed point search stage, a tensor low-rank sparse decomposition model is first used to decompose the IR image into target and background images. To reduce residual noise and background edges in the target image, we designed a partial tensor correlated total variation (PTCTV) norm. This norm effectively constrains the global low-rankness and local smoothness of the background, and enhances the model's focus on image detail information. Subsequently, leveraging the global sparsity of the target, a density peak search technique is employed to locate seed points in the target image. In the seed point discrimination stage, k-means clustering is utilized to improve the accuracy of the local contrast measure (LCM) in scenarios with uncertain target distribution and mixed background components. By calculating the LCM for each seed point, we further suppress background clutter and enhance real targets. Extensive experiments demonstrate that the proposed method exhibits superior overall performance compared to advanced methods and achieves satisfactory computational speed.

关键词： Tensors Noise Clutter TV Image edge detection Gray-scale Filtering Density peak search infrared (IR) small target detection k-means clustering local contrast measure (LCM) partial tensor correlated total variation (PTCTV)

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Analyzing Cost Overrun Risks in Construction Projects: A Multi-Stakeholder Perspective Using Fuzzy Group Decision-Making and k-means clustering

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BUILDINGS 2025年第3期15卷 447-447页

作者： Abdelalim, Ahmed Mohammed Salem, Maram Salem, Mohamed Al-Adwani, Manal Tantawy, Mohamed Helwan Univ Mataria Branch Fac Engn Civil Engn Dept Project Management & Sustainable Construct Program POB 11718 Cairo Egypt Helwan Univ Mataria Branch Fac Engn Civil Engn Dept PON 11718 Cairo Egypt Australian Univ Kuwait Coll Engn Dept Civil Engn Safat 13015 Kuwait Int Univ Kuwait IUK Adjunct Fac Civil & Architectural Engn Ardiya 92400 Kuwait

The current research investigates cost overrun factors in structural projects, focusing on the Middle East and North Africa (MENA) region using Egypt as a model. A systematic literature review was conducted, analyzing 405 research papers published between 2000 and 2024, from which 69 relevant papers were selected to identify 48 key factors contributing to cost overrun. Using k-means clustering, these factors were grouped into three clusters based on their probability and impact, which were classified for their risk levels. To ensure robust analysis, a survey was conducted to gather expert opinions, resulting in 369 valid responses from owners, contractors/subcontractors, and management firms/consultants. The fuzzy group decision-making approach (FGDMA) was conducted to rank all 48 factors, offering a detailed assessment of their relative importance. Based on these rankings, the top 20 factors were identified for analysis to examine variations in stakeholder priorities, capturing differences in perspectives among multi-stakeholders. Sensitivity analysis and Tornado charts explored the critical variations among stakeholders, with management firms/consultants and owners prioritizing design-related risks, such as inconsistencies and delays in approvals, while contractors/subcontractors focused more on material waste. This novel integration presents a structured approach for analyzing, prioritizing, and mitigating cost overrun risks, offering a comprehensive framework that provides practical insights for stakeholders to improve cost and risk management strategies.

关键词： cost overrun construction fuzzy group decision-making approach k-means clustering sensitivity tornado chart risk management

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Optimizing Solar Energy Harvesting: A k-means clustering Approach for Enhanced Efficiency and Viability

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ENERGY SCIENCE & ENGINEERING 2025年第1期13卷 191-202页

作者： Bin, Li Shahzad, Muhammad Farhan, Muhammad khan, Muhammad Sanaullah Saif, Mubaarak Abdulrahman Abdu Bitew, Girmaw Teshager North China Elect Power Univ Sch Elect & Elect Engn Beijing Peoples R China Muhammad Nawaz Sharif Univ Engn & Technol Dept Elect Engn Multan Pakistan Govt Coll Univ Faisalabad Dept Elect Engn & Technol Faisalabad Pakistan Taiz Univ Fac Engn & Informat Technol Renewable Energy Dept Taizi Yemen Bahir Dar Univ Bahir Dar Inst Technol Fac Elect & Comp Engn Bahir Dar Ethiopia

In recent years, photovoltaic (PV) solar energy has played a crucial role in the global transition toward renewable energy, contributing to 46% of the electric capacity. It has emerged as a primary source;however, optimizing energy utilization and solar panel efficiency to maximize absorbed solar radiation remains a significant challenge. Additionally, it addresses the optimization of solar energy generation and the mitigation of potential overheating issues in dual-axis solar tracking systems. Despite its importance, PV power generation is hindered by uncertainty and intermittency, posing obstacles to achieving a stable and reliable power supply. This research introduces an innovative synthesis method for a typical solar radiation year (TSRY) based on k-means clustering to maximize energy harvest. The k-means algorithm, a fundamental image processing technique, is utilized to classify images into distinct groups. This approach enhances energy generation potential, panel efficiency, and the long-term sustainability of solar energy systems compared to conventional methods.

关键词： distributed generation k-means clustering maximum power point tracking photovoltic energy renewable energy sustainable energy

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High-accuracy cell discrimination based on multiple regression combined with k-means clustering algorithm for lithium-ion rechargeable cells

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JOURNAL OF POWER ELECTRONICS 2025年第4期25卷 709-723页

作者： Lee, Pyeong-Yeon Han, Dongho kim, Jonghoon Chungnam Natl Univ Dept Elect Engn Daejeon South Korea

Inhomogeneity in battery pack unit cells significantly impacts their lifetime, performance, and safety. Consistent electrochemical characteristics are essential to mitigate this issue, and minimizing manufacturing tolerances is critical. This study introduces a novel approach that integrates multi-parameter screening with k-means clustering to enhance battery pack design. In contrast to traditional single-parameter sorting, which limits precision and increases performance inconsistencies, the proposed method considers a wider range of factors, including open-circuit voltage, voltage change, capacity, and partial capacity, ensuring more balanced and consistent performance while effectively addressing outliers. An analysis of 800 commercial batteries revealed high correlation factors via statistical methods. Multiple regression analysis was used to quantify their effects, and a clustering algorithm was employed to group cells with similar characteristics. The effectiveness of the proposed method was validated through electrical characteristic tests by comparing the designed battery packs.

关键词： Cell matching Lithium-ion cell Multiple regression analysis k-means clustering

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A Data-Driven Algorithm for Dynamic Parameter Estimation of an Alkaline Electrolysis System Combining Online Reinforcement Learning and k-means clustering Analysis

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PROCESSES 2025年第4期13卷 1009-1009页

作者： Sun, Zexian Zhang, Tao Zhang, Jiaming Zhao, Mingyu Wan, Zhiyu Chen, Honglei North China Univ Sci & Technol Coll Elect Engn Tangshan 063509 Peoples R China Caofeidian Coll Technol Coll Elect Engn Tangshan 063200 Peoples R China Hebei Univ Engn Sci Coll Elect Engn Shijiazhuang 056038 Peoples R China

Determining the electrochemical, thermal, and mass transfer dynamics embedded in an alkaline electrolysis (AEL) system provides important information about the application of ancillary services provided by hydrogen energy for the elimination of carbon emissions. Therefore, there is an urgent need to develop methodologies for evaluating key parameters, such as overvoltage coefficients, stack transfer capacity, diaphragm thickness, and permeability, to accurately capture the system's fluctuating characteristics. However, limited by the lack of superior sensor technology, some significant variables cannot be measured directly. In this context, comprehensively accurate parameters of an estimation strategy offer a novel alternative to characterize the system's corresponding intrinsic nature. This paper was motivated by this arduous challenge and aims to address the large branching factors with irregular properties. Specifically, the associated mathematical models reflecting the transient operating parameters in terms of electrochemical, heat transfer, and mass transfer are first established. Subsequently, k-means clustering analysis is conducted to deduce the similarity of distribution of the measured variables, which can function as proxies of the separator to distinguish the working status. Furthermore, online reinforcement learning (RL), renowned for its ability to operate without extensive predefined datasets, is employed to conduct dynamic parameter estimation, thereby approximating the robust nonlinear and stochastic behaviors within AEL components. Finally, the experimental results verify that the proposed model achieves significant improvements in estimation errors compared to existing parameter estimation methods (such as EkF and UkF). The enhancements are 76.7%, 54.96%, 51.84%, and 31% in terms of RMSE, NRMSE, PCC, and MPE, respectively.

关键词： dynamic parameter estimation alkaline electrolysis system k-means clustering online reinforcement learning

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Classification and Prediction of AC Contactor Degradation States Based on k-means clustering and Bayes-BiLSTM

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2025年第3期20卷 1899-1910页

作者： Liu, Shuxin Li, Yankai Peng, Shidong Cao, Yundong Shenyang Univ Technol Coll Elect Engn Key Lab Special Machine & High Voltage Apparat Shenyang 110870 Peoples R China

Aiming at the problems of difficulty in characterizing the contactor degradation process by a single characteristic parameter, lack of basis for contactor degradation state delineation, and low state prediction accuracy, this paper proposes an AC contactor degradation state delineation and prediction method based on k-means clustering and Bayes optimized Bi-directional long-short term memory neural network (BiLSTM). Firstly, the signal data of the full-life test process of the contactor are collected to extract the electrical and mechanical parameters of the contactor. Then, the degradation indexes of mechanical and electrical parameters are constructed by principal component analysis (PCA), and the joint characteristic vectors are constructed. Secondly, the optimal contactor degradation state is classified by k-means clustering. Finally, the Bayes-BiLSTM method is built to predict the degradation state of the contactor. After comparative analysis, it can be seen that the method of this paper can better complete the division and prediction of the contactor degradation state, the degradation state of 4 presents the optimal clustering effect, and the average prediction accuracy of the degradation state is 97.3%.

关键词： AC contactors k-means clustering Bayes optimization BiLSTM State partitioning and prediction

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