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Evaluation of k-means algorithm for Faulted Landforms Extraction and Offset Measurement With an Example From the Eastern kunlun Fault

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IEEE ACCESS 2025年 13卷 47848-47859页

作者： Zhou, Shengchao Lin, Zhou Zhejiang Univ Sch Earth Sci Hangzhou 310012 Peoples R China

Accurate offset measurement is crucial for recovering the size of past earthquakes and understanding the recurrence patterns of strike-slip faults. Traditional methods, which rely on manual delineation of displaced geomorphic markers from satellite images, often introduce significant uncertainties. This study aims to develop a more objective and accurate method for identifying faulted landforms and measuring offsets. Although supervised deep learning methods have great potential for image recognition and segmentation, due to the absence of data sets, we apply the k-means algorithm, an easy and practical unsupervised machine learning method with minimal parameters, to extract displaced geomorphic markers. Our research is conducted in the kusai Lake segment of the Eastern kunlun Fault using high-resolution satellite imagery. Initially, we identify multiple well-preserved geomorphic markers along the fault traces and manually label some to form an imagery dataset. The k-means algorithm demonstrates its efficacy in extracting landforms clearly when the classification number is set to three. Furthermore, a quantitative comparison of several commonly used unsupervised algorithms confirmed that k-means performs best, achieving a recall of 0.70 and an accuracy of 0.906 on the dataset. The validity of our measurements is corroborated by examining two specific sites around Hongshui Gou. Quantitative analysis comparing our imagery-based measurements with field data reveals a strong agreement, evidenced by a Pearson's correlation coefficient of 0.997. Repeated measurements of the same geomorphic markers across different images indicate that the geomorphic features obtained from the images significantly affect the accuracy of offset measurements. This study underscores the potential of our approach for both extracting faulted landforms and accurately measuring their offsets, emphasizing the importance of assessing the integrity of geomorphic markers when using satellite imagery

关键词： Satellite images Earthquakes Lakes Seismic measurements Fault diagnosis Accuracy Clustering algorithms Vectors Geologic measurements Machine learning Strike-slip fault offset measurement k-means algorithm Eastern kunlun fault

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Design and implementation of parallel k-means algorithm based on ternary optical computer

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JOURNAL OF SUPERCOMPUTING 2025年第4期81卷 1-24页

作者： Song, kai Zhang, Haiming Ma, Huaqiong Sun, Yongjun Yan, Liping East China Jiaotong Univ Sch Informat Engn Shuanggang Rd Nanchang 330013 Peoples R China East China Jiaotong Univ Software Sch Shuanggang Rd Nanchang 330013 Peoples R China

The k-means algorithm is one of the most commonly used clustering algorithms in the field of artificial intelligence. However, as the amount of data increases, traditional electronic computers face challenges such as low computational efficiency and high energy consumption when executing the k-means algorithm. The ternary optical computer (TOC), as a novel optoelectronic hybrid computer, employs optical processing and electrical control to perform data computations. This type of computer features a large number of data bits, carry-free addition, and low power consumption, making it well-suited to address the issues encountered by electronic computers in implementing the k-means algorithm. Leveraging the characteristics of TOC, such as its abundant data bits and reconfigurable processor, this paper designs a parallel k-means algorithm based on TOC. Additionally, this paper also proposes an implementation method for a TOC-based subtractor. This subtractor eliminates the need to take the opposite of the subtrahend, thus making the TOC more convenient for implementing the k-means algorithm. Finally, experimental validation was conducted on the TOC experimental platform. The results demonstrate that the implementation of the subtractor is correct and that the parallel k-means algorithm on TOC is feasible. Through performance comparison, it is shown that the time complexity of the multiplication process in calculating the distance between sample points and cluster centers can be optimized from On2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$O\left( {{n<^>2}} \right)$$\end{document} to On\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-6

关键词： Modified signed digit Ternary optical computer SJ-MSD adder k-means algorithm

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Research on Traffic Dynamic Shortest Path Allocation Model Based on Triangular Fuzzy Number Weight and k-means algorithm

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Sun, Hanzheng Li, Dan Zhi, Baoping Ren, Yongming Cheng, Xingyan Yellow River Conservancy Tech Inst Sch Civil Engn & Transportat Engn Kaifeng 475004 Peoples R China

Aiming at the traffic dynamic shortest path allocation problem with triangular fuzzy numbers as attribute values, a traffic dynamic shortest path allocation model method based on triangular fuzzy number weights is proposed. The weighted distance optimization model of triangular fuzzy number, ideal solution and negative ideal solution is established. The weight value of each attribute is obtained by solving and optimizing. The similarity difference between attribute information is determined longitudinally by using the idea of deviation maximization, and the evaluation uncertainty of each scheme under different attributes is described horizontally by entropy value. The attribute weights based on reliability are obtained by synthesizing the similarity difference index and the uncertainty index. Based on triangular fuzzy weights. A subset of vertex set V, R, is selected as the representative vertex, and the shortest path distance between all vertex pairs in R is calculated. At the same time, considering the spatial correlation of road traffic flow and the dynamic change process of traffic flow in each section of the road network, the time is discretized, and the abrupt point of road traffic state is taken as the node pair, and the road traffic flow is dynamically loaded. The prediction model of road section travel time under occasional congestion is established. Experiments show that when the error upper limit is small, the retrieval results of the approximation algorithm are relatively accurate. Then, an example analysis of traffic dynamic path allocation is provided, which shows the effectiveness and feasibility of this method in traffic dynamic shortest path allocation.

关键词： Heuristic algorithms Decision making Roads Dynamic scheduling Resource management Complexity theory Uncertainty Shortest path problem Approximation algorithms Planning Triangular fuzzy number weight k-means algorithm traffic dynamic shortest path traffic flow

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Method for Solving Large Linear Algebraic Equation Systems Based on kaczmarz-k-means algorithm

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JOURNAL OF APPLIED MATHEMATICS 2025年第1期2025卷

作者： Li, Hao Baoding Open Univ Student Affairs Off Baoding Peoples R China

In response to the problems in solving large-scale linear algebraic equations, this study adopts two block discrimination criteria, the Euclidean clustering and the cosine clustering, to decompose them into row vectors and construct the k-means clustering algorithm. Based on the uniformly distributed block kaczmarz algorithm, the weight coefficients are integrated into the probability criterion to construct a new and efficient weight probability. Thus, a block kaczmarz algorithm based on weight probability is constructed. Experimental results showed that the method was three to five times faster than the greedy kaczmarz method. In any convergence situation, the uniformly distributed kaczmarz method had smaller computational complexity and iteration times compared with the weighted probability block kaczmarz method, with a minimum acceleration ratio of 2.21 and a maximum acceleration ratio of 8.44. This study can establish and analyze mathematical models of various complex systems, reduce memory consumption and computation time when solving large linear equation systems, and provide solutions and scientific guidance for solving large linear equation systems in fields such as civil engineering and electronic engineering.

关键词： kaczmarz algorithm k-means algorithm linear equation system solution

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The personalised classification of brand promotion information based on k-means algorithm

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International Journal of Information Technology and Management 2025年第1-2期24卷 117-129页

作者： Li, Xi Department of Art and Design Guangxi University of Foreign Languages Nanning Guangxi530222 China

In order to improve the efficiency of personalised classification of brand promotion information and shorten the time of personalised classification, this paper proposes a personalised classification method of brand promotion information based on k-means algorithm. First, collect brand promotion information, and calculate the text relevance of brand promotion information through weighting factors. Secondly, the attribute division of extension information is carried out by using the three branch decision-making theory. Then, the information features of brand promotion are extracted by capsule network. Finally, calculate the similarity between different brand promotion information, obtain the brand promotion information classification function, and realise the personalised classification of brand promotion information through k-means algorithm. The experimental results show that the classification accuracy of this method is 98.08%, and the time of personalised information classification is only 1.20 s, indicating that this method can effectively improve the efficiency of personalised classification of brand promotion information. © 2025 Inderscience Enterprises Ltd.

关键词： feature extraction information attribute division k-means algorithm personalised classification of information

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A k-means algorithm for Digital Management of Urban Planning Engineering 3rd

A K-means Algorithm for Digital Management of Urban Planning...

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3rd World Conference on Intelligent and 3D Technologies, WCI3DT 2024

作者： Wang, Chenlin Xi ‘an Jiaotong University City College Xi ‘an China

ISBN: (纸本)9789819791231

The background of digital management of urban planning engineering comes from the acceleration of urbanization and the continuous rise of population density, which brings challenges to urban governance. At the same time, the development of digital technology has also provided new solutions for urban governance. The digital management system of urban planning engineering has serious inefficiency and management error rate. In this paper, k-means algorithm is used to establish the digital management system of urban planning engineering, and the conclusion is drawn through the experiment. The algorithm greatly reduces the error rate, and its accuracy rate reaches 98.6, which improves the efficiency of digital management of urban planning engineering. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Digital management k-means algorithm Machine learning Quality assessment Urban planning engineering

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Cluster Analysis of Poverty Data in Cities/Districts in Indonesia Using k-means algorithm for the Years 2019–2022 8th

Cluster Analysis of Poverty Data in Cities/Districts in Indo...

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8th International Conference on Intelligent Technologies, ICIT 2023

作者： Salomo, Julian Siregar, Bakti Matana University Kab. Tangerang Indonesia

ISBN: (纸本)9789819738588

This article focuses on clustering 514 cities/districts in Indonesia based on their poverty levels from the pre-COVID-19 period (2019) until the near-end of the COVID-19 period (2022). Using publicly available data from the Central Statistics Agency (BPS) for 2019–2022, encompassing 11 poverty-related variables, the study employs unsupervised learning techniques. The process involves testing for multicollinearity;normalizing the data and applying Principal Component Analysis to reduce the variables to 2 principal components;determining the optimal number of clusters (3) is done using the elbow method, silhouette index, and gap statistic algorithm;the k-means algorithm is then applied to cluster the cities/districts each year. The analysis reveals three clusters: cluster 1 represents areas with low poverty levels, cluster 2 comprises regions with moderate levels, and cluster 3 includes areas with high poverty rates. The number of cities/districts in cluster 3 increased from 36 in 2019 to 40 in 2022, with 32 consistently belonging to this cluster. These findings highlight the importance of targeted government assistance to these areas. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cluster optimization k-means algorithm PCA Poverty data Unsupervised learning

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Analysis and Investigation of Babakoohi Anticline Fractures Based on Clustering Technique Using k-means and Genetic algorithm, and Paleostress Determination Using MIM Method, Shiraz, Iran

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DOkLADY EARTH SCIENCES 2025年第1期520卷 1-13页

作者： kazemi, Hajar Yazdjerdi, kouros Asadi, Abdolmajid Mozafari, Mohammad Reza Islamic Azad Univ Fars Sci & Res Branch Dept Geol Fars Iran Islamic Azad Univ Dept Geol Shiraz Branch Shiraz Iran Islamic Azad Univ Dept Math Shiraz Branch Shiraz Iran

Geological structural data related to joints and faults can be effectively analysed using statistical methods and techniques to improve the quality of the data and achieve accurate analysis regarding the applied stress in an area. Clustering is considered one of the ideal methods of organizing data or information, with the purpose of optimizing, analyzing the data, identifying accurate patterns consistent with the data, and establishing effective connections between data for precise analysis. k-means and genetic algorithms are two of the most important and practical fuzzy algorithms in statistical methods that have been used to cluster data from Babakohi anticline valleys located in the north of Shiraz, Iran. The results of applying this technique and geological structural studies show the Babakohi anticline data as two distinct gatherings or clusters. In order to investigate the accuracy of the number of clusters identified, VW, ICC, VMPC, and VPBMF validation indices were also analysed. These indicators also confirm the two clusters. The specified stress phases in the region by the MIM method (multiple inverse techniques) based on clustering using fault scratch slip data show two stress phases: tensile and strike-slip. The integration of the joint clustering results using the mentioned algorithms and determining the stress phases indicates the action of the two stress phases in the Babakohi anticline.

关键词： Babakoohi Fractures clustering k-means algorithm Genetic algorithm MIM

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k-means algorithm Based on Particle Swarm Optimization for the Identification of Rock Discontinuity Sets

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ROCk MECHANICS AND ROCk ENGINEERING 2015年第1期48卷 375-385页

作者： Li, Yanyan Wang, Qing Chen, Jianping Xu, Liming Song, Shengyuan Jilin Univ Coll Construct Engn Changchun 130026 Peoples R China

Discontinuities within a rock mass strongly influence the behavior of the rock mass (kulatilake and Wu 1984; Zhou and Maerz 2002), endowing the rock mass with discontinuous, non-homogeneous, and anisotropic features (Zhang et al. 2012). Therefore, collecting and analyzing discontinuity data within rock masses carefully has significant meaning for engineering applications (Hammah and Curran 1998). Engineers try to delineate discontinuities into subsets based on similar orientations when interpreting discontinuity data.

关键词： Discontinuity sets k-means algorithm Particle swarm optimization (PSO) Discontinuity orientation

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k-means algorithm for the detection and delineation of QRS-complexes in Electrocardiogram

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IRBM 2010年第1期31卷 48-54页

作者： Mehta, S. S. Shete, D. A. Lingayat, N. S. Chouhan, V. S. Dr Babasaheb Ambedkar Technol Univ Inst Petrochem Engn Dept Elect Engn Lonere 402103 Maharashtra India JN Vyas Univ Dept Elect Engn MBM Engn Coll Jodhpur 342001 Rajasthan India Inst Engn & Technol Dept Elect & Telecommun Engn Alwar 301030 Rajasthan India

Electrocardiogram (ECG) is an important bioelectrical signal used to asses the cardiac state of a patient It consists of a recurrent wave sequence of P-wave, QRS-complex and T-wave associated with each beat The QRS-complex is the prominent feature of the ECG. This paper presents a simple method using k-means clustering algorithm for the detection of QRS-complexes in ECG signal. Digital filters are used to remove the power line interference and baseline wander present in the ECG signal k-means algorithm is used to classify QRS and non-QRS-region in the ECG signal The performance of the algorithm is validated using dataset-3 of the CSE multi-lead measurement library Detection rate of 98.66% is obtained The percentage of false positive and false negative is 1.14% and 1.34% respectively. The mean and standard deviation of the errors between automatic and manual annotations is calculated to validate the delineation performance of the algorithm. The on sets and offsets of the detected QRS-complexes are found well within the tolerance limits its specified by the CSE library. (C) 2009 Elsevier Masson SAS All rights reserved.

关键词： ECG QRS-complex k-means algorithm ECG delineation

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