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Relevant bibliographies by topics / KMeans Clustering Method

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Academic literature on the topic 'KMeans Clustering Method'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "KMeans Clustering Method"

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Musdar, Izmy Alwiah, and Azhari Azhari. "RCE-Kmeans Method for Data Clustering." International Journal of Advances in Intelligent Informatics 1, no. 2 (2015): 107. http://dx.doi.org/10.26555/ijain.v1i2.38.

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There have been many methods developed to solve the clustering problem. One of them is method in swarm intelligence field such as Particle Swarm Optimization (PSO). Rapid Centroid Estimation (RCE) is a method of clustering based Particle Swarm Optimization. RCE, like other variants of PSO clustering, does not depend on initial cluster centers. Moreover, RCE has faster computational time than the previous method like PSC and mPSC. However, RCE has higher standar deviation value than PSC and mPSC in which has impact in the variance of clustering result. It is happaned because of improper equilib

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Lin, Xiaohui, and Jianmin Xu. "Road network partitioning method based on Canopy-Kmeans clustering algorithm." Archives of Transport 54, no. 2 (2020): 95–106. http://dx.doi.org/10.5604/01.3001.0014.2970.

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With the increasing scope of traffic signal control, in order to improve the stability and flexibility of the traffic control system, it is necessary to rationally divide the road network according to the structure of the road network and the characteristics of traffic flow. However, road network partition can be regarded as a clustering process of the division of road segments with similar attributes, and thus, the clustering algorithm can be used to divide the sub-areas of road network, but when Kmeans clustering algorithm is used in road network partitioning, it is easy to fall into the loc

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Musdar, Izmy Alwiah, and Azhari SN. "Metode RCE-Kmeans untuk Clustering Data." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 9, no. 2 (2015): 157. http://dx.doi.org/10.22146/ijccs.7544.

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AbstrakTelah banyak metode yang dikembangkan untuk memecahkan berbagai masalah clustering. Salah satunya menggunakan metode-metode dari bidang kecerdasan kelompok seperti Particle Swarm Optimization (PSO). Metode Rapid Centroid Estimation (RCE) merupakan salah satu metode clustering yang berbasis PSO. RCE, seperti varian PSO clustering lainnya, memiliki kelebihan yaitu hasil clustering tidak tergantung pada inisialisasi pusat cluster awal. RCE juga memiliki waktu komputasi yang jauh lebih cepat dibandingkan dengan metode sebelumnya yaitu Particle Swarm Clustering (PSC) dan modified Particle Sw

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Huang, Wenna, Yong Peng, Yuan Ge, and Wanzeng Kong. "A new Kmeans clustering model and its generalization achieved by joint spectral embedding and rotation." PeerJ Computer Science 7 (March 30, 2021): e450. http://dx.doi.org/10.7717/peerj-cs.450.

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The Kmeans clustering and spectral clustering are two popular clustering methods for grouping similar data points together according to their similarities. However, the performance of Kmeans clustering might be quite unstable due to the random initialization of the cluster centroids. Generally, spectral clustering methods employ a two-step strategy of spectral embedding and discretization postprocessing to obtain the cluster assignment, which easily lead to far deviation from true discrete solution during the postprocessing process. In this paper, based on the connection between the Kmeans clu

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Zhu-Juan Ma, Zhu-Juan Ma, Zi-Han Wang Zhu-Juan Ma, Xiang-Hua Chen Zi-Han Wang, and Feng Liu Xiang-Hua Chen. "DP-Kmeans and Beyond: Optimal Clustering with a new Clustering Validity Index." 電腦學刊 33, no. 5 (2022): 001–17. http://dx.doi.org/10.53106/199115992022103305001.

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<p>The K-means clustering algorithm is widely used in many areas for its high efficiency. However, the performance of the traditional K-means algorithm is very sensitive to the selection of initial clustering centers. Furthermore, except the convex distributed datasets, the traditional K-means algorithm still cannot optimally process many non-convex distributed datasets and datasets with outliers. To this end, this paper proposes the DP-Kmeans, an improved K-means algorithm based on the Density Parameter and center replacement, which can be more accurate than the traditional K-means by d

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Zhao, Huiling. "Design and Implementation of an Improved K-Means Clustering Algorithm." Mobile Information Systems 2022 (September 22, 2022): 1–10. http://dx.doi.org/10.1155/2022/6041484.

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Aiming at the problems of the traditional K-means clustering algorithm, such as the local optimal solution and the slow clustering speed caused by the uncertainty of k value and the randomness of the initial cluster center selection, this paper proposes an improved KMeans clustering method. The algorithm first uses the idea of the elbow rule based on the sum of squares of errors to obtain the appropriate number of clusters k, then uses the variance as a measure of the degree of dispersion of the samples, and selects k data points with the smallest variance and the distance greater than the ave

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Waode, Yully Sofyah, Anang Kurnia, and Yenni Angraini. "K-Means Optimization Algorithm to Improve Cluster Quality on Sparse Data." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 23, no. 3 (2024): 641–52. http://dx.doi.org/10.30812/matrik.v23i3.3936.

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The aim of this research is clustering sparse data using various K-Means optimization algorithms. Sparse data used in this research came from Citampi Stories game reviews on Google Play Store. This research method are Density Based Spatial Clustering of Applications with Noise-Kmeans (DB-Kmeans), Particle Swarm Optimization-Kmeans (PSO-Kmeans), and Robust Sparse Kmeans Clustering (RSKC) which are evaluated using the silhouette score. Clustering sparse data presented a challenge as it could complicate the analysis process, leading to suboptimal or non-representative results. To address this cha

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Karthika, N., and B. Janet. "Feature Pair Index Graph for Clustering." Journal of Intelligent Systems 29, no. 1 (2019): 1179–87. http://dx.doi.org/10.1515/jisys-2018-0338.

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Abstract Text documents are significant arrangements of various words, while images are significant arrangements of various pixels/features. In addition, text and image data share a similar semantic structural pattern. With reference to this research, the feature pair is defined as a pair of adjacent image features. The innovative feature pair index graph (FPIG) is constructed from the unique feature pair selected, which is constructed using an inverted index structure. The constructed FPIG is helpful in clustering, classifying and retrieving the image data. The proposed FPIG method is validat

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Trushali, Jambudi, and Gandhi Savita. "An Effective Initialization Method Based on Quartiles for the K-means Algorithm." Indian Journal of Science and Technology 15, no. 35 (2022): 1712–21. https://doi.org/10.17485/IJST/v15i35.714.

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Abstract <strong>Objectives:</strong> This study aims to speed up the K-means algorithm by offering a deterministic quartile-based seeding strategy for initializing preliminary cluster centers for the K-means algorithm, enabling it to efficiently build high-quality clusters. <strong>Methods:</strong> We have investigated various cluster center initialization approaches in literature and presented our findings. For the Kmeans algorithm, we here propose a novel deterministic technique based on quartiles for finding initial cluster centers. To obtain the preliminary cluster centers

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Hao, Zheng. "Railway Passenger Customer Segmentation Method Based on User Preferences." Advanced Materials Research 850-851 (December 2013): 1028–31. http://dx.doi.org/10.4028/www.scientific.net/amr.850-851.1028.

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This paper uses the clustering model SOM-Kmeans two segment to cluster the users on railway ticket selling system. First, it describes the types of customer segmentation and the general segmentation steps, then introduces the definition of user preferences, at last on the basis of calculation steps of SOM-Kmeans two segment algorithm, the customer segmentation model and algorithm is given based on user preferences.

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Book chapters on the topic "KMeans Clustering Method"

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Du, Zhijiao, and Sumin Yu. "Trust Cop-Kmeans Clustering Method." In Social Network Large-Scale Decision-Making. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7794-9_4.

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Ni, Weijun, Shuai Guo, Liupeng Wang, and Aixin Li. "Optimization Method of Drilling Rig Scheduling Task Assignment Based on Kmeans-ACO Algorithm." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde221121.

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Aiming at the lack of research on the assignment model of drilling rig scheduling tasks in China, and it is difficult to meet the field requirements of this model in practical applications, a drilling rig scheduling task assignment optimization scheme combining Kmeans algorithm and ACO algorithm is established. Firstly, the wellhead coordinate data in the well site is divided into blocks using the clustering feature of Kmeans, and the algorithm optimization coefficient is determined by the required number of wells in the block. Secondly, the ant colony algorithm is used to calculate and plan t

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Karami, Amin. "A Novel Fuzzy Anomaly Detection Algorithm Based on Hybrid PSO-Kmeans in Content-Centric Networking." In Advances in Computational Intelligence and Robotics. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9474-3.ch017.

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In Content-Centric Networks (CCNs) as a promising network architecture, new kinds of anomalies will arise. Usually, clustering algorithms would fit the requirements for building a good anomaly detection system. K-means is a popular anomaly detection method; however, it suffers from the local convergence and sensitivity to selection of the cluster centroids. This chapter presents a novel fuzzy anomaly detection method that works in two phases. In the first phase, authors propose an hybridization of Particle Swarm Optimization (PSO) and K-means algorithm with two simultaneous cost functions as w

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Lamere, Alicia Taylor. "Cluster Analysis in R With Big Data Applications." In Research Anthology on Bioinformatics, Genomics, and Computational Biology. IGI Global, 2023. http://dx.doi.org/10.4018/979-8-3693-3026-5.ch022.

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This chapter discusses several popular clustering functions and open source software packages in R and their feasibility of use on larger datasets. These will include the kmeans() function, the pvclust package, and the DBSCAN (density-based spatial clustering of applications with noise) package, which implement K-means, hierarchical, and density-based clustering, respectively. Dimension reduction methods such as PCA (principle component analysis) and SVD (singular value decomposition), as well as the choice of distance measure, are explored as methods to improve the performance of hierarchical

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Lamere, Alicia Taylor. "Cluster Analysis in R With Big Data Applications." In Open Source Software for Statistical Analysis of Big Data. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2768-9.ch004.

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This chapter discusses several popular clustering functions and open source software packages in R and their feasibility of use on larger datasets. These will include the kmeans() function, the pvclust package, and the DBSCAN (density-based spatial clustering of applications with noise) package, which implement K-means, hierarchical, and density-based clustering, respectively. Dimension reduction methods such as PCA (principle component analysis) and SVD (singular value decomposition), as well as the choice of distance measure, are explored as methods to improve the performance of hierarchical

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Anagnostopoulos, Theodoros. "Reinforcement and Non-Reinforcement Machine Learning Classifiers for User Movement Prediction." In Intelligent Technologies and Techniques for Pervasive Computing. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-4038-2.ch012.

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Mobile context-aware applications are required to sense and react to changing environment conditions. Such applications, usually, need to recognize, classify, and predict context in order to act efficiently, beforehand, for the benefit of the user. In this chapter, the authors propose a mobility prediction model, which deals with context representation and location prediction of moving users. Machine Learning (ML) techniques are used for trajectory classification. Spatial and temporal on-line clustering is adopted. They rely on Adaptive Resonance Theory (ART) for location prediction. Location

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Conference papers on the topic "KMeans Clustering Method"

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Vidhya, J. V., and R. Annie Uthra. "Video Summarization through Latent Representation: A VAE and KMeans Clustering Method." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724134.

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Zhang, Huiben, Chunyu Liu, Mengmeng Zhang, and Ruifeng Zhu. "A hot spot clustering method based on improved kmeans algorithm." In 2017 14th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). IEEE, 2017. http://dx.doi.org/10.1109/iccwamtip.2017.8301443.

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Lu, Shizeng, Hongliang Yu, Xiaohong Wang, et al. "Clustering Method of Raw Meal Composition Based on PCA and Kmeans." In 2018 37th Chinese Control Conference (CCC). IEEE, 2018. http://dx.doi.org/10.23919/chicc.2018.8482823.

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Liu, Tao. "Research on Naive Bayes Integration Method based on Kmeans++ digital teaching clustering." In 2023 8th International Conference on Information Systems Engineering (ICISE). IEEE, 2023. http://dx.doi.org/10.1109/icise60366.2023.00069.

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Cui, Jin, Lei Ren, Lin Zhang, and Qiong Wu. "An Optimal Allocation Method for Virtual Resource Considering Variable Metrics of Cloud Manufacturing Service." In ASME 2015 International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/msec2015-9245.

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Based on the concept of Cloud Computing, a new service-oriented, high efficiency low consumption, knowledge-based, and intelligent networked agile manufacturing model Cloud Manufacturing (CMfg) has been proposed recently. The manufacturing resources optimization allocation model (MROAM) is one of the core parts for implementing CMfg. In this paper, a new MROAM is proposed in the background of CMfg system. In this model, variable metrics such as a variety of evaluation indicators for different types of manufacturing services are taken into account. In addition, time, cost, virtual manufacturing

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Khasanov, M. B., and S. A. K. Diane. "Segmentation and Visualization of Water Pollution Based on the K-means Method." In 33rd International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2023. http://dx.doi.org/10.20948/graphicon-2023-363-370.

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The paper presents a study of the current state of water pollution detection systems. A formalization of the centroid map for a three-channel aerial photograph is proposed. An example of using the Kmeans algorithm for clustering terrain and water areas on test aerial photographs is considered. The visualization of the results of clustering of aerial photographs for a different number of centroids is given as well as the results of pollution segmentation. A block diagram of the clustering algorithm is presented. Its advantages and disadvantages are identified. The structure of the developed sof

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Muoghalu, Amalachukwu Immaculata. "A Machine Learning Approach to Rock Typing with Relative Permeability Curves Using Kmeans Clustering Algorithm." In SPE Annual Technical Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/212383-stu.

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Abstract Relative permeability is an important petrophysical property that characterizes the dynamic movement of one fluid to another during the production of oil and/or gas in the subsurface. Numerous methods for rock typing exist in the industry, but classification based on the effect of relative permeability is often deficient. These rock typing methods which are manually modeled or simulated can still be erroneous as the rocks are characterized based on human observation. Therefore, there is a need for a simple yet accurate rock typing method that automatically classifies the rocks based o

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Righi, Marcelo Antonio, and Raul Ceretta Nunes. "Detecção de DDoS Através da Análise da Quantificação da Recorrência Baseada na Extração de Características Dinâmicas e Clusterização Adaptativa." In Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais. Sociedade Brasileira de Computação - SBC, 2016. http://dx.doi.org/10.5753/sbseg.2016.19321.

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The high number of Distributed Denial of Service (DDoS) attacks have demanded innovative solutions to guarantee reliability and availability of internet services. In this sense, different methods have been used to analyze network traffic for denial of service attacks, such as neural networks, decision trees, principal component analysis and others. However, few of them explore dynamic features to classify network traffic. This article proposes a new method, called DDoSbyAQR,that uses the recurrence quantification analysis based on the extraction of dynamic characteristics and an adaptive clust

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Gonzalez, Keyla, and Siddharth Misra. "Rapid Time-Lapse Monitoring of Geological Carbon Storage." In SPE EuropEC - Europe Energy Conference featured at the 84th EAGE Annual Conference & Exhibition. SPE, 2023. http://dx.doi.org/10.2118/214405-ms.

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Abstract Precision monitoring of the subsurface carbon-dioxide plume ensures long-term, sustainable geological carbon storage at a large scale. Electrical resistivity tomography (ERT) can accurately map the evolution of the CO2 saturation during geological carbon storage. To better monitor the CO2 plume migration in a storage reservoir, we develop an unsupervised spatiotemporal clustering to process the CO2 saturation maps derived from the ERT measurements acquired over 80 days. Using dynamic time wrapping (DTW) Kmeans clustering, four distinct clusters were identified in the CO2-storage reser

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Li, Zhichuan, Zijian Zhang, Xinhai Lei, Jingshui Lao, and Ya Li. "Integrating Dual-Parameter Soil Classification with CPT-Driven Machine Learning for Site Investigation of Offshore Wind." In Offshore Technology Conference. OTC, 2024. http://dx.doi.org/10.4043/35306-ms.

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Abstract Accurate geological characterization is crucial in offshore engineering projects. This study aims to develop an advanced methodology that extends the application of Cone Penetration Testing (CPT), providing a more precise and detailed classification of subsurface soils in the locations without coring. Then, the methodology integrates advanced techniques to achieve precise geological characterization. It initiates with a dual-parameter clustering analysis, a process pivotal in uncovering nuanced geological insights. Submerged unit weight and undrained shear strength, recognized as prim

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