Relevant bibliographies by topics / K-Means Cluster (K-means)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'K-Means Cluster (K-means)'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "K-Means Cluster (K-means)"

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Hedar, Abdel-Rahman, Abdel-Monem Ibrahim, Alaa Abdel-Hakim, and Adel Sewisy. "K-Means Cloning: Adaptive Spherical K-Means Clustering." Algorithms 11, no. 10 (2018): 151. http://dx.doi.org/10.3390/a11100151.

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We propose a novel method for adaptive K-means clustering. The proposed method overcomes the problems of the traditional K-means algorithm. Specifically, the proposed method does not require prior knowledge of the number of clusters. Additionally, the initial identification of the cluster elements has no negative impact on the final generated clusters. Inspired by cell cloning in microorganism cultures, each added data sample causes the existing cluster ‘colonies’ to evaluate, with the other clusters, various merging or splitting actions in order for reaching the optimum cluster set. The propo
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de Maeyer, Rieke, Sami Sieranoja, and Pasi Fränti. "Balanced k-means revisited." Applied Computing and Intelligence 3, no. 2 (2023): 145–79. http://dx.doi.org/10.3934/aci.2023008.

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<abstract><p>The $ k $-means algorithm aims at minimizing the variance within clusters without considering the balance of cluster sizes. Balanced $ k $-means defines the partition as a pairing problem that enforces the cluster sizes to be strictly balanced, but the resulting algorithm is impractically slow $ \mathcal{O}(n^3) $. Regularized $ k $-means addresses the problem using a regularization term including a balance parameter. It works reasonably well when the balance of the cluster sizes is a mandatory requirement but does not generalize well for soft balance requirements. In
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Hetangi, D. Mehta* Daxa Vekariya Pratixa Badelia. "COMPARISON AND EVALUATION OF CLUSTER BASED IMAGE SEGMENTATION TECHNIQUES." Global Journal of Engineering Science and Research Management 4, no. 12 (2017): 24–33. https://doi.org/10.5281/zenodo.1098696.

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Image segmentation is the classification of an image into different groups. Numerous algorithms using different approaches have been proposed for image segmentation. A major challenge in segmentation evaluation comes from the fundamental conflict between generality and objectivity. A review is done on different types of clustering methods used for image segmentation. Also a methodology is proposed to classify and quantify different clustering algorithms based on their consistency in different applications. There are different methods and one of the most popular methods is k-means clustering al
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Rosmayati, Mohemad, Naziah Mohd Muhait Nazratul, Maizura Mohamad Noor Noor, and Ali Othman Zulaiha. "Performance analysis in text clustering using k-means and k-medoids algorithms for Malay crime documents." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (2022): 5014–26. https://doi.org/10.11591/ijece.v12i5.pp5014-5026.

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Few studies on text clustering for the Malay language have been conducted due to some limitations that need to be addressed. The purpose of this article is to compare the two clustering algorithms of k-means and k-medoids using Euclidean distance similarity to determine which method is the best for clustering documents. Both algorithms are applied to 1,000 documents pertaining to housebreaking crimes involving a variety of different modus operandi. Comparability results indicate that the k-means algorithm performed the best at clustering the relevant documents, with a 78% accuracy rate. K-mean
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Sihombing, Pardomuan Robinson, Yoshep Paulus Apri Caraka Yuda, Busminoloan Busminoloan, and Iis Hayyun Nurul Islam. "KOMPARASI PERFORMA K-MEANS DAN FUZZY C-MEANS." Jurnal Bayesian : Jurnal Ilmiah Statistika dan Ekonometrika 2, no. 2 (2022): 125–32. http://dx.doi.org/10.46306/bay.v2i2.35.

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This study aims to test the performance of the K-Means Cluster method with Fuzzy C-Means. The data used is data from the Inclusive Economic Development Index in 34 provinces in Indonesia in 2021. The data is sourced from Bappenas. The optimum number of clusters suggested using the Elbow method technique is as many as 4 clusters. By paying attention to the silouhette value the K-Means method is as good as the Fuzzi C-Means. However, the K-Means method is better than the Fuzzy C-Means model when viewed based on the criteria of smaller AIC and BIC values and a larger R 2. The provinces of Papua a
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Zahir, Zainuddin, and Alviadi Nur Risal Andi. "Balanced clustering for student admission school zoning by parameter tuning of constrained k-means." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 2301–13. https://doi.org/10.11591/ijai.v13.i2.pp2301-2313.

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The Indonesian government issued a regulation through the Ministry of Education and Culture, number 51 of 2018, which contains zoning rules to improve the quality of education in school educational institutions. This research aims to compare the performance of the k-means algorithm with the constrained k-means algorithm to model the zoning of each school area based on the shortest distance parameter between the school location and the domicile of prospective students. The study used data from 2,248 prospective students and 22 public school locations. The results of testing the k-means algorith
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Pham, D. T., S. S. Dimov, and C. D. Nguyen. "An Incremental K-means algorithm." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 218, no. 7 (2004): 783–95. http://dx.doi.org/10.1243/0954406041319509.

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Data clustering is an important data exploration technique with many applications in engineering, including parts family formation in group technology and segmentation in image processing. One of the most popular data clustering methods is K-means clustering because of its simplicity and computational efficiency. The main problem with this clustering method is its tendency to coverge at a local minimum. In this paper, the cause of this problem is explained and an existing solution involving a cluster centre jumping operation is examined. The jumping technique alleviates the problem with local
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Gubu, La, Edi Cahyono, Arman, Herdi Budiman, and Muh Kabil Djafar. "CLUSTER ANALYSIS FOR MEAN-VARIANCE PORTFOLIO SELECTION: A COMPARISON BETWEEN K-MEANS AND K-MEDOIDS CLUSTERING." Jurnal Riset dan Aplikasi Matematika (JRAM) 7, no. 2 (2023): 104–15. https://doi.org/10.26740/jram.v7n2.p104-115.

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This paper presents the Mean-Variance (MV) portfolio selection using cluster analysis. Stocks are categorized into various clusters using K-Means and K-Medoids clustering. Based on the Sharpe ratio, a stock from each cluster is chosen to represent that cluster. Stocks with the greatest Sharpe ratio are those that are chosen for each cluster. With the guidance of the MV portfolio model, the optimum portfolio is identified. When there are many stocks included in the formation of the portfolio, we may efficiently create the optimal portfolio using this method. For the empirical study, the daily r
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Tri Gustiane, Indri, Martanto Martanto, and Tati Suprapti. "CLUSTERING HASIL CEK DARAH DIABETES LANSIA MENGGUNAKAN METODE K-MEANS DI POSBINDU KP. LEBAKJERO DESA CIHERANG." JATI (Jurnal Mahasiswa Teknik Informatika) 8, no. 2 (2024): 2125–29. http://dx.doi.org/10.36040/jati.v8i2.9281.

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Penelitian ini bertujuan untuk menganalisis hasil cek darah lansia yang menderita diabetes menggunakan metode K-Means. Diabetes adalah penyakit metabolic yang ditandai dengan tingginya kadar gula darah (hiperglikemia) yang disebabkan oleh kekurangan insulin atau tidak efektif insulin dalam mengatur metabolisme glukosa. Selain itu terdapat faktor-faktor lain menjadi penyebab terjadinya diabetes diantaranya seperti faktor keturunan, berat badan, usia, tekanan darah dan sebagainya. Diabetes penyakit kronis yang umumnya terjadi pada lansia dan membutuhkan pemantauan berkala untuk mengelola kondisi
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Rifa, Isna Hidayatur, Hasih Pratiwi, and Respatiwulan Respatiwulan. "CLUSTERING OF EARTHQUAKE RISK IN INDONESIA USING K-MEDOIDS AND K-MEANS ALGORITHMS." MEDIA STATISTIKA 13, no. 2 (2020): 194–205. http://dx.doi.org/10.14710/medstat.13.2.194-205.

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Earthquake is the shaking of the earth's surface due to the shift in the earth's plates. This disaster often happens in Indonesia due to the location of the country on the three largest plates in the world and nine small others which meet at an area to form a complex plate arrangement. An earthquake has several impacts which depend on the magnitude and depth. This research was, therefore, conducted to classify earthquake data in Indonesia based on the magnitudes and depths using one of the data mining techniques which is known as clustering through the application of k-medoids and k-means algo
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Dissertations / Theses on the topic "K-Means Cluster (K-means)"

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Hong, Sui. "Experiments with K-Means, Fuzzy c-Means and Approaches to Choose K and C." Honors in the Major Thesis, University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/1224.

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This item is only available in print in the UCF Libraries. If this is your Honors Thesis, you can help us make it available online for use by researchers around the world by following the instructions on the distribution consent form at http://library.ucf<br>Bachelors<br>Engineering and Computer Science<br>Computer Engineering
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Thibodeau, Éric. "Profiling and optimizing K-means algorithms in a beowulf cluster environment." Mémoire, École de technologie supérieure, 2009. http://espace.etsmtl.ca/85/1/THIBODEAU_%C3%89ric.pdf.

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L'algorithme d'agglomeration statistique K-means sert a classer des bases de donnees non libellees en K groupes. Faisant partie de la fonction d'evaluation d'un Algorithme Ecolutionnaire (AE), I'optimisation de ce dernier est devenu un point d'interet. Malgre les multiples approches proposees pour son optimisation et sa parallelisation, tres pen de recherche s'est attardee aux questions entourant la performance et I'efficacite parallele des implantations. Dans la plupart des cas, les descriptions entourant I'environnement d'execution demeurent opaques et la presentation precise de profil
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Zhao, Jianmin. "Optimal Clustering: Genetic Constrained K-Means and Linear Programming Algorithms." VCU Scholars Compass, 2006. http://hdl.handle.net/10156/1583.

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Quinteiro, José António Teixeira. "Segmentação de individuos no Facebook que gostam de música: abordagem exploratória, recorrendo à comparação entre dois algoritmos, k-means e fuzzy c-means." Master's thesis, Instituto Superior de Economia e Gestão, 2011. http://hdl.handle.net/10400.5/4338.

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Mestrado em Gestão/MBA<br>Para se poder definir os melhores planos estratégicos, as decisões de marketing que se têm que tomar, com o intuito de abordar o mercado, escolher a melhor campanha publicitária, seleccionar o segmento e o tipo de produto ou serviço a oferecer, têm que ter por base o resultado de uma boa análise técnica da informação ou dos dados disponíveis. A escolha do método de segmentação, é de primordial importância, pois os dados que se obtêm podem alterar a estratégia de selecção do mercado alvo e a estratégia de posicionamento dos produtos ou serviços, para além dos custos in
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CALENDER, CHRISTOPHER R. "APPROXIMATE N-NEAREST NEIGHBOR CLUSTERING ON DISTRIBUTED DATABASES USING ITERATIVE REFINEMENT." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1092929952.

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Reanier, Richard Eugene. "Refinements to K-means clustering : spatial analysis of the Bateman site, arctic Alaska /." Thesis, Connect to this title online; UW restricted, 1992. http://hdl.handle.net/1773/6420.

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Castillo, Gregorio Alfonso. "A K-MEANS BASED WATERSHED IMAGING SEGMENTATION ALGORITHM FOR BANANA CLUSTER QUALITY INSPECTION." OpenSIUC, 2016. https://opensiuc.lib.siu.edu/theses/2037.

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Banana has become the most commonly consumed fresh fruit among US population. It is a challenge to use computer vision to divide touching bananas, for this purpose a novel image segmentation algorithm is proposed, combining k-means and the watershed transformation. The first part is to extract the background, achieved using a K-means based in the HS space, the second part is individual banana segmentation where a smarter selection of the initial markers from where the watershed transformation grows is attained fusing two morphological filters with different structural elements. The validation
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Schorsch, Andrea. "Statistische Eigenschaften von Clusterverfahren." Master's thesis, Universität Potsdam, 2008. http://opus.kobv.de/ubp/volltexte/2009/2902/.

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Die vorliegende Diplomarbeit beschäftigt sich mit zwei Aspekten der statistischen Eigenschaften von Clusterverfahren. Zum einen geht die Arbeit auf die Frage der Existenz von unterschiedlichen Clusteranalysemethoden zur Strukturfindung und deren unterschiedlichen Vorgehensweisen ein. Die Methode des Abstandes zwischen Mannigfaltigkeiten und die K-means Methode liefern ausgehend von gleichen Daten unterschiedliche Endclusterungen. Der zweite Teil dieser Arbeit beschäftigt sich näher mit den asymptotischen Eigenschaften des K-means Verfahrens. Hierbei ist die Menge der optimalen Clusterzentren
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Hou, Jun. "Using Hadoop to Cluster Data in Energy System." University of Dayton / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1430092547.

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Rogers, Matthew Alan. "Properties of the tropical hydrologic cycle as analyzed through 3-dimensional k-means cluster analysis." online access from Digital Dissertation Consortium, 2008. http://libweb.cityu.edu.hk/cgi-bin/er/db/ddcdiss.pl?3332703.

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Books on the topic "K-Means Cluster (K-means)"

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Deruelle, Nathalie, and Jean-Philippe Uzan. Cosmological perturbations. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198786399.003.0061.

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This chapter describes the first steps toward an understanding of large structures, which are observed in the universe at all scales—galaxies, groups of galaxies, and galactic clusters. It does so by studying the evolution of perturbations at linear order in Friedmann–Lemaître spacetimes. To simplify the discussion, the chapter limits the scope to the textbook case where the spatial sections of the background space are Euclidean (K = 0), and anisotropic perturbations and entropy perturbations are absent. This basically means that the matter reduces to a single fluid. The relativistic and Newto
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Book chapters on the topic "K-Means Cluster (K-means)"

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Bezdek, James C. "The c-Means (aka k-Means) Models." In Elementary Cluster Analysis. River Publishers, 2022. http://dx.doi.org/10.1201/9781003338086-8.

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Jain, Manan B., Kapil G. Ratan, and Rashmi Benni. "Quantum-Inspired Cluster Optimization: K-Means Versus Quantum K-Means." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2694-6_18.

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Kłopotek, Mieczysław A., Sławomir T. Wierzchoń, and Robert A. Kłopotek. "k-means Cluster Shape Implications." In IFIP Advances in Information and Communication Technology. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49161-1_10.

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Khandelwal, Siddhesh, and Amit Awekar. "Faster K-Means Cluster Estimation." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56608-5_43.

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Wu, Junjie. "Cluster Analysis and K-means Clustering: An Introduction." In Advances in K-means Clustering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29807-3_1.

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Wang, Jingdong, Jing Wang, Qifa Ke, Gang Zeng, and Shipeng Li. "Fast Approximate $$K$$ K -Means via Cluster Closures." In Multimedia Data Mining and Analytics. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14998-1_17.

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Cheung, Yiu-ming. "k*-Means — A Generalized k-Means Clustering Algorithm with Unknown Cluster Number." In Intelligent Data Engineering and Automated Learning — IDEAL 2002. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45675-9_48.

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Reddy, Damodar, Devender Mishra, and Prasanta K. Jana. "MST-Based Cluster Initialization for K-Means." In Advances in Computer Science and Information Technology. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17857-3_33.

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Li, Yu-guang, and Yong-sheng Huang. "Product Requirements Cluster Analysis Based on K-Means." In Proceedings of 20th International Conference on Industrial Engineering and Engineering Management. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40063-6_46.

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Lee Hernández, Luis Earving, José Antonio Castán-Rocha, Salvador Ibarra-Martínez, Jésus David Terán-Villanueva, Mayra Guadalupe Treviño-Berrones, and Julio Laria-Menchaca. "Cluster Analysis Using k-Means in School Dropout." In Studies in Big Data. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-38325-0_1.

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Conference papers on the topic "K-Means Cluster (K-means)"

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Wu, Shixun, Yitong Ding, Yujia Zhai, et al. "FT K-Means: A High-Performance K-Means on GPU with Fault Tolerance." In 2024 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2024. http://dx.doi.org/10.1109/cluster59578.2024.00035.

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Zhakubayev, Alibek, and Greg Hamerly. "Beta k-Means: Accelerating k-Means Using Probabilistic Cluster Filtering." In 2024 IEEE 11th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2024. http://dx.doi.org/10.1109/dsaa61799.2024.10722834.

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Khan, Mozammel H. A. "Internal-Cluster-Validation-Based Model Selection For k-Means Clustering." In 2024 28th International Computer Science and Engineering Conference (ICSEC). IEEE, 2024. https://doi.org/10.1109/icsec62781.2024.10770636.

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Xu, Mengxue. "Cluster Analysis of Online Learning Behavior Based on K-Means." In 2025 IEEE International Conference on Electronics, Energy Systems and Power Engineering (EESPE). IEEE, 2025. https://doi.org/10.1109/eespe63401.2025.10987550.

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Wu, Ruhao, Fengchao Chen, Qiwei Li, Ruifeng Zhao, and Zheng Liu. "Distributed Energy Cluster Partition and Generation Planning Based on Improved K-Means++." In 2024 3rd International Conference on Energy and Electrical Power Systems (ICEEPS). IEEE, 2024. http://dx.doi.org/10.1109/iceeps62542.2024.10693182.

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Ruswanti, Diyah, Ichwan Joko Prayitno, and Firdhaus Hari Saputra Al Haris. "Optimizing Cluster Methods: Combining K-Means with Hierarchical Techniques for Better Results." In 2024 6th International Conference on Cybernetics and Intelligent System (ICORIS). IEEE, 2024. https://doi.org/10.1109/icoris63540.2024.10903822.

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Bie, Mu, Lian Tang, and Hang Lin. "Research on K-means Optimized Cluster Analysis Method for Agricultural Housing Construction Data." In 2024 IEEE 5th International Conference on Pattern Recognition and Machine Learning (PRML). IEEE, 2024. https://doi.org/10.1109/prml62565.2024.10779900.

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Wijayanti, Sefty, Azahari, and Reza Andrea. "K-Means Cluster Analysis for Students Graduation." In the 2017 International Conference. ACM Press, 2017. http://dx.doi.org/10.1145/3108421.3108430.

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Jing Wang, Jingdong Wang, Qifa Ke, Gang Zeng, and Shipeng Li. "Fast approximate k-means via cluster closures." In 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2012. http://dx.doi.org/10.1109/cvpr.2012.6248034.

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Zhou, Tao, Yan-Ning Zhang, He-Jing Yuan, and Hui-Ling Lu. "Rough k-means Cluster with Adaptive Parameters." In Sixth International Conference on Machine Learning Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370674.

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Reports on the topic "K-Means Cluster (K-means)"

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Bejarano, Jeremy, Koushiki Bose, Tyler Brannan, et al. Sampling Within k-Means Algorithm to Cluster Large Datasets. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1025410.

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Kryzhanivs'kyi, Evstakhii, Liliana Horal, Iryna Perevozova, Vira Shyiko, Nataliia Mykytiuk, and Maria Berlous. Fuzzy cluster analysis of indicators for assessing the potential of recreational forest use. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/4470.

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Cluster analysis of the efficiency of the recreational forest use of the region by separate components of the recreational forest use potential is provided in the article. The main stages of the cluster analysis of the recreational forest use level based on the predetermined components were determined. Among the agglomerative methods of cluster analysis, intended for grouping and combining the objects of study, it is common to distinguish the three most common types: the hierarchical method or the method of tree clustering; the K-means Clustering Method and the two-step aggregation method. For
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Cordeiro de Amorim, Renato. A survey on feature weighting based K-Means algorithms. Web of Open Science, 2020. http://dx.doi.org/10.37686/ser.v1i2.79.

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In a real-world data set there is always the possibility, rather high in our opinion, that different features may have different degrees of relevance. Most machine learning algorithms deal with this fact by either selecting or deselecting features in the data preprocessing phase. However, we maintain that even among relevant features there may be different degrees of relevance, and this should be taken into account during the clustering process. With over 50 years of history, K-Means is arguably the most popular partitional clustering algorithm there is. The first K-Means based clustering algo
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Reifenstuhl, R. R., R. J. Newberry, S. A. Haug, K. H. Clautice, S. A. Liss, and F. R. Weber. Chert geochemistry discriminant analysis and K-means cluster analysis: Rampart project area, Tanana B-1 Quadrangle, east-central Alaska. Alaska Division of Geological & Geophysical Surveys, 2009. http://dx.doi.org/10.14509/19341.

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Iurasova, Olga, Larysa Ivashko, Oleksandr Maksymov, and Julia Maksymova. Impact of Return on Education on Economic Growth in EU Countries. Vilnius Business College, 2024. https://doi.org/10.57005/ab.2024.2.6.

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The objective of this article is to assess the impact of returns on the education and professional skills of workers on economic growth in EU countries. Based on open data, two principal components were formed to identify the aggregated influence of selected indicators on GDP growth. These principal components allow for the evaluation of the degree of influence of education and professional skills of workers on GDP growth for each country. Countries were clustered according to the degree of influence of the obtained principal components on the level of economic development using the k-means me
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Puzon, Klarizze Anne Martin. Democracy clusters and patterns of inequality: A k-means approach. UNU-WIDER, 2023. http://dx.doi.org/10.35188/unu-wider/2023/380-2.

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