Relevant bibliographies by topics / Fuzzy K-means

Contents

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

Academic literature on the topic 'Fuzzy K-means'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Fuzzy K-means"

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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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Hutin, Adam Al Avin Faisal. "Clustering Job Seekers in Bojonegoro Using K-Means and Fuzzy K-Means." Jurnal Statistika dan Komputasi 4, no. 1 (2025): 33–46. https://doi.org/10.32665/statkom.v4i1.4651.

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Background: Job seekers are part of the labor force who are unemployed and actively looking for work. One of the efforts to address the rising number of job seekers is by expanding job openings or employment opportunities. Employment is an essential need for individuals to meet various aspects of life, ranging from basic needs to education and housing. Objective: This paper aims to analyze the frequency distribution of job seeker attributes in Bojonegoro, compare the K-Means and Fuzzy K-Means methods in clustering sub-districts, determine the best clustering method, and describe frequency dist
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Lee, Soo-Hyun, Jae-Yun Kim, and Young-Seon Jeong. "Various Validity Indices for Fuzzy K-means Clustering." korean management review 46, no. 4 (2017): 1201–26. http://dx.doi.org/10.17287/kmr.2017.46.4.1201.

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Banumathi, A., and A. Pethalakshmi. "Refinement of K-Means and Fuzzy C-Means." International Journal of Computer Applications 39, no. 17 (2012): 11–16. http://dx.doi.org/10.5120/4911-7441.

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R, Jayasree, and A. Sheela Selvakumari N. "Analyzing Student Performance using Fuzzy Possibilistic C-Means Clustering Algorithm." Indian Journal of Science and Technology 16, no. 38 (2023): 3230–35. https://doi.org/10.17485/IJST/v16i38.226.

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Abstract <strong>Objectives:</strong>&nbsp;This work is to propose a more effective Fuzzy C-means clustering algorithm for predicting student performance based on their health.&nbsp;<strong>Methods:</strong>&nbsp;The standard dataset is collected from UCI repository. This study proposes FPCM-SPP clustering algorithm which is compared with traditional algorithms like K-Means, K-Medoids, and Fuzzy C-Means using student data from secondary education at two Portuguese institutions (2008). Based on the clustering accuracy, mean squared error, and cluster formation time, the performance of the clust
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Liu, Bowen, Ting Zhang, Yujian Li, Zhaoying Liu, and Zhilin Zhang. "Kernel Probabilistic K-Means Clustering." Sensors 21, no. 5 (2021): 1892. http://dx.doi.org/10.3390/s21051892.

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Kernel fuzzy c-means (KFCM) is a significantly improved version of fuzzy c-means (FCM) for processing linearly inseparable datasets. However, for fuzzification parameter m=1, the problem of KFCM (kernel fuzzy c-means) cannot be solved by Lagrangian optimization. To solve this problem, an equivalent model, called kernel probabilistic k-means (KPKM), is proposed here. The novel model relates KFCM to kernel k-means (KKM) in a unified mathematic framework. Moreover, the proposed KPKM can be addressed by the active gradient projection (AGP) method, which is a nonlinear programming technique with co
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Rahmah, Lathifatur. "PERBANDINGAN HASIL PENGGEROMBOLAN K-MEANS, FUZZY K-MEANS, DAN TWO STEP CLUSTERING." Jurnal Pendidikan Matematika 2, no. 1 (2017): 39. http://dx.doi.org/10.18592/jpm.v2i1.1166.

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Analisis gerombol merupakan salah satu metode peubah ganda yang tujuan utamanya adalah mengelompokkan objek berdasarkan kemiripan atau ketidakmiripan karakteristik-karakteristiknya, sehingga objek yang terletak dalam satu gerombol memiliki kemiripan sifat yang lebih besar dibandingkan dengan objek pengamatan yang terletak pada gerombol lain. K-means merupakan salah satu metode penggerombolan tak berhirarki yang paling banyak digunakan, namun karena menggunakan rataan sebagai centroidnya, metode ini lebih sensitif terhadap keberadaan pencilan pada data. Sehingga berkembanglah metode baru, k-med
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Hot, Elma, and Vesna Popovic-Bugarin. "Soil data clustering by using K-means and fuzzy K-means algorithm." Telfor Journal 8, no. 1 (2016): 56–61. http://dx.doi.org/10.5937/telfor1601056h.

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Nuraeni, Fitri, Helfy Susilawati, and Yoga Handoko Agustin. "PERBANDINGAN IMPLEMENTASI ALGORITMA K-MEANS++ DAN FUZZY C-MEANS PADA SEGMENTASI CITRA WAJAH." JuTI "Jurnal Teknologi Informasi" 1, no. 2 (2023): 47. http://dx.doi.org/10.26798/juti.v1i2.722.

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Dalam pengenalan wajah menggunakan metode pengolahan citra, dibutuhkan proses segmentasi citra agar dapat dilakukan proses analisis citra selanjutnya. Segmentasi citra dapat dilakukan dengan metode clustering yang memiliki beberapa algoritma berbasis centroid, seperti k-means dan fuzzy c-means. Algoritma k-means sendiri memiliki beberapa varian, salah satunya k-means++ dimana varian ini lebih cerdas dalam memilih inisial centroid dibanding k-means yang memilih inisial centroid secara acak. Algoritma fuzzy cmeans sendiri telah memiliki keunggulan dalam engelompokan objek yang tersebar secara ti
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Shinde, Ganeshchandra Narharrao, Inamdar S. A., and Narangale S.M. "Fuzzy Mean Point Clustering using K-means algorithm for implementing the movecentroid function code." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 1 (2013): 54–56. http://dx.doi.org/10.24297/ijct.v4i1b.3059.

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The paper focus on combination of K-Means algorithm for Fuzzy Mean Point Clustering Neural Network (FMPCNN). The algorithm is implemented in JAVA program code for implementing the movecentroid function code into FMPCNN. Here we have provided movecentroid’s output to Fuzzy clustering as criteria, movecentroid is the base function of K-means algorithm as in Fuzzy Mean Point Clustering Neural Network (FMPCNN) algorithm, calculation of cluster based on pre-defined criteria and scope is done. In the experiment we have used four datasets and observed results in nano seconds there is huge differe
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Dissertations / Theses on the topic "Fuzzy 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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Camara, Assa. "Využití fuzzy množin ve shlukové analýze se zaměřením na metodu Fuzzy C-means Clustering." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-417051.

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This master thesis deals with cluster analysis, more specifically with clustering methods that use fuzzy sets. Basic clustering algorithms and necessary multivariate transformations are described in the first chapter. In the practical part, which is in the third chapter we apply fuzzy c-means clustering and k-means clustering on real data. Data used for clustering are the inputs of chemical transport model CMAQ. Model CMAQ is used to approximate concentration of air pollutants in the atmosphere. To the data we will apply two different clustering methods. We have used two different methods to s
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Schenatto, Kelyn. "Utilização de métodos de interpolação e agrupamento para definição de unidades de manejo em agricultura de precisão." Universidade Estadual do Oeste do Parana, 2014. http://tede.unioeste.br:8080/tede/handle/tede/178.

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Made available in DSpace on 2017-05-12T14:46:57Z (GMT). No. of bitstreams: 1 Kelyn Schenatto.pdf: 4212903 bytes, checksum: 0ba04350cc25aff5e6acb249938e5375 (MD5) Previous issue date: 2014-02-04<br>Despite the benefits offered by the technology of precision agriculture (PA), the necessity of dense sampling grids and use of sophisticated equipment for the soil and plant handling make it financially unfeasible in many cases, especially for small producers. With the aimof making viable the PA, the definition of management zones (MZ) consists in dividing the plotin subregions that have similar ph
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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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Klen, André Monteiro. "Algoritmo para agrupamento de descontinuidades em famílias baseado no Método Fuzzy K-Means." reponame:Repositório Institucional da UFOP, 2015. http://www.repositorio.ufop.br/handle/123456789/5705.

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Programa de Pós-Graduação em Geotecnia. Núcleo de Geotecnia, Escola de Minas, Universidade Federal de Ouro Preto.<br>Submitted by Oliveira Flávia (flavia@sisbin.ufop.br) on 2015-10-16T16:50:52Z No. of bitstreams: 2 license_rdf: 19418 bytes, checksum: 6dde0d96f18aca4c252a500311f54121 (MD5) TESE_AlgoritmoAgrupamentoDescontinuidades.pdf: 4101321 bytes, checksum: 68c6eec732bdc99941712e49b4cf8c3e (MD5)<br>Approved for entry into archive by Gracilene Carvalho (gracilene@sisbin.ufop.br) on 2015-10-29T18:48:25Z (GMT) No. of bitstreams: 2 license_rdf: 19418 bytes, checksum: 6dde0d96f18aca4c252a500311f
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Chahine, Firas Safwan. "A Genetic Algorithm that Exchanges Neighboring Centers for Fuzzy c-Means Clustering." NSUWorks, 2012. http://nsuworks.nova.edu/gscis_etd/116.

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Clustering algorithms are widely used in pattern recognition and data mining applications. Due to their computational efficiency, partitional clustering algorithms are better suited for applications with large datasets than hierarchical clustering algorithms. K-means is among the most popular partitional clustering algorithm, but has a major shortcoming: it is extremely sensitive to the choice of initial centers used to seed the algorithm. Unless k-means is carefully initialized, it converges to an inferior local optimum and results in poor quality partitions. Developing improved method for se
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Drach, Tetjana Oleksandrivna, and Oleksandr Evgenovich Goloskokov. "Research and development of mathematical and software solutions of the information system of situational enterprise management." Thesis, NTU "KhPI", 2018. http://repository.kpi.kharkov.ua/handle/KhPI-Press/38079.

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Zubková, Kateřina. "Text mining se zaměřením na shlukovací a fuzzy shlukovací metody." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2018. http://www.nusl.cz/ntk/nusl-382412.

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This thesis is focused on cluster analysis in the field of text mining and its application to real data. The aim of the thesis is to find suitable categories (clusters) in the transcribed calls recorded in the contact center of Česká pojišťovna a.s. by transferring these textual documents into the vector space using basic text mining methods and the implemented clustering algorithms. From the formal point of view, the thesis contains a description of preprocessing and representation of textual data, a description of several common clustering methods, cluster validation, and the application its
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LIMA, Flávia Ayana Nascimento de. "Agrupamento de fornos de redução de alumínio utilizando os algoritmos Affinity Propagation, Mapa auto–organizável de Kohonen (som), Fuzzy C–Means e K–Means." Universidade Federal do Pará, 2017. http://repositorio.ufpa.br/jspui/handle/2011/9470.

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Submitted by Marina Farias (mgmf@ufpa.br) on 2018-02-21T13:19:33Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Dissertacao_AgrupamentoFornosReducao.pdf: 6297988 bytes, checksum: 9e3c95180dbdfbdbc60f142c239aeb87 (MD5)<br>Approved for entry into archive by Marina Farias (mgmf@ufpa.br) on 2018-02-21T13:23:06Z (GMT) No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Dissertacao_AgrupamentoFornosReducao.pdf: 6297988 bytes, checksum: 9e3c95180dbdfbdbc60f142c239aeb87 (MD5)<br>Made available in DSpace on 2018-
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Nguiffo, Podie Yves. "Segmentation d'images de transmission pour la correction de l'atténué en tomographie d'émission par positrons." Mémoire, Université de Sherbrooke, 2009. http://savoirs.usherbrooke.ca/handle/11143/3993.

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L'atténuation des photons est un phénomène qui affecte directement et de façon profonde la qualité et l'information quantitative obtenue d'une image en Tomographie d'Emission par Positrons (TEP). De sévères artefacts compliquant l'interprétation visuelle ainsi que de profondes erreurs d'exactitudes sont présents lors de l'évaluation quantitative des images TEP, biaisant la vérification de la corrélation entre les concentrations réelles et mesurées.L' atténuation est due aux effets photoélectrique et Compton pour l'image de transmission (30 keV - 140 keV), et majoritairement à l'effet Compton p
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Book chapters on the topic "Fuzzy K-means"

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Wu, Junjie. "Generalizing Distance Functions for Fuzzy c-Means Clustering." In Advances in K-means Clustering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29807-3_3.

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Hou, Min, Shibin Zhang, and Jinyue Xia. "Quantum Fuzzy K-Means Algorithm Based on Fuzzy Theory." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06794-5_28.

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Yue, Guanli, Yanpeng Qu, and Ansheng Deng. "Fuzzy Dissimilarity Measure Based K-Means Clustering." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70665-4_193.

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Wu, Rui, and Peilin Shi. "Clustering Web Transactions Using Fuzzy Rough− k Means." In Advances in Software Engineering. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10242-4_19.

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Panda, Sandeep, Sanat Sahu, Pradeep Jena, and Subhagata Chattopadhyay. "Comparing Fuzzy-C Means and K-Means Clustering Techniques: A Comprehensive Study." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30157-5_45.

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de Lima, Flávia A. N., Alan M. F. de Souza, Fábio M. Soares, Diego Lisboa Cardoso, and Roberto C. L. de Oliveira. "Clustering Aluminum Smelting Potlines Using Fuzzy C-Means and K-Means Algorithms." In Light Metals 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51541-0_73.

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Upadhyay, Anand, Bipinkumar Yadav, Kirti Singh, and Varun Shukla. "COVID-19 Data Clustering Using K-means and Fuzzy c-means Algorithm." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7346-8_46.

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Ren, Zhiliang, and Mengyuan Chen. "Hierarchical Normal Wiggly Hesitant Fuzzy K-means Clustering Algorithm." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-6934-6_62.

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Kenger, Omer Nedim, and Eren Ozceylan. "A Comparative Analysis of Fuzzy C-Means, K-Means, and K-Medoids Clustering Algorithms for Analysis Countries’ COVID-19 Risk." In Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85626-7_4.

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Purnawansyah, Haviluddin, Achmad Fanany Onnilita Gafar, and Imam Tahyudin. "Comparison Between K-Means and Fuzzy C-Means Clustering in Network Traffic Activities." In Proceedings of the Eleventh International Conference on Management Science and Engineering Management. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59280-0_24.

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Conference papers on the topic "Fuzzy K-means"

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Santos, Gabriel Machado, Rita Maria Silva Julia, and Marcelo Zanchetta Do Nascimento. "K-GBS3FCM - KNN Graph-Based Safe Semi-Supervised Fuzzy C-Means." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825467.

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Sinambela, Marzuki, Hanifullah Hafidz Arrizal, Eva Darnila, Munawar, Muchamad Rizqy Nugraha, and Anton Widodo. "Clustering Analysis of Earthquake Based on K-Means, DBSCAN, and Fuzzy C-Means in North Sumatra." In 2024 International Conference on Information Technology and Computing (ICITCOM). IEEE, 2024. https://doi.org/10.1109/icitcom62788.2024.10762334.

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Singh, Vivek Kumar, Nisha Tiwari, and Shekhar Garg. "Document Clustering Using K-Means, Heuristic K-Means and Fuzzy C-Means." In 2011 International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2011. http://dx.doi.org/10.1109/cicn.2011.62.

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Xhafa, Fatos, Adriana Bogza, Santi Caballe, and Leonard Barolli. "Apache Mahout's k-Means vs Fuzzy k-Means Performance Evaluation." In 2016 International Conference on Intelligent Networking and Collaborative Systems (INCoS). IEEE, 2016. http://dx.doi.org/10.1109/incos.2016.103.

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Wei Wang, Shimin Wei, Qizheng Liao, Yaqin Xia, Danlin Li, and Junzi Li. "Fuzzy K-means clustering on infrasound sample." In 2008 IEEE 16th International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2008. http://dx.doi.org/10.1109/fuzzy.2008.4630455.

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Banerjee, Shreya, Ankit Choudhary, and Somnath Pal. "Empirical evaluation of K-Means, Bisecting K-Means, Fuzzy C-Means and Genetic K-Means clustering algorithms." In 2015 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE). IEEE, 2015. http://dx.doi.org/10.1109/wiecon-ece.2015.7443889.

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Matsui, Tomohiro, Katsuhiro Honda, Chi-Hyon Oh, Akira Notsu, and Hidetomo Ichihashi. "Cluster validation in k-Means clustering based on PCA-guided k-Means and procrustean transformation of PC scores." In 2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2009. http://dx.doi.org/10.1109/fuzzy.2009.5277333.

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Reddy Poli, Venkata Subba. "Fuzzy C-Means and Fuzzy K-Means Algorithms using Fuzzy Functional Dependencies." In 2022 International Conference on Fuzzy Theory and Its Applications (iFUZZY). IEEE, 2022. http://dx.doi.org/10.1109/ifuzzy55320.2022.9985227.

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Honda, Katsuhiro, Ryoichi Nonoguchi, Akira Notsu, and Hidetomo Ichihashi. "PCA-guided k-Means clustering with incomplete data." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007312.

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Kapoor, Akanksha, and Abhishek Singhal. "A comparative study of K-Means, K-Means++ and Fuzzy C-Means clustering algorithms." In 2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT). IEEE, 2017. http://dx.doi.org/10.1109/ciact.2017.7977272.

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Reports on the topic "Fuzzy K-means"

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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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