Relevant bibliographies by topics / Fuzzyc- means clustering

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  2. Dissertations / Theses
  3. Books
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Academic literature on the topic 'Fuzzyc- means clustering'

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

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Journal articles on the topic "Fuzzyc- means clustering"

1

Miyamoto, Sadaaki. "Formulation of Fuzzyc-Means Clustering Using Calculus of Variations and Twofold Membership Clusters." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 5 (2008): 454–60. http://dx.doi.org/10.20965/jaciii.2008.p0454.

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A membership matrix of fuzzyc-means clustering is associated with corresponding fuzzy classification rules as membership functions defined on the whole data space. We directly derive such functions in fuzzyc-means and possibilistic clustering using the calculus of variations, generalizing ordinary fuzzyc-means and deriving new twofold membership clustering using this framework.
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Hamasuna, Yukihiro, Yasunori Endo, and Sadaaki Miyamoto. "On Tolerant Fuzzyc-Means Clustering." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 4 (2009): 421–28. http://dx.doi.org/10.20965/jaciii.2009.p0421.

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This paper presents a new type of clustering algorithms by using a tolerance vector called tolerant fuzzyc-means clustering (TFCM). In the proposed algorithms, the new concept of tolerance vector plays very important role. In the original concept of tolerance, a tolerance vector attributes to each data. This concept is developed to handle data flexibly, that is, a tolerance vector attributes not only to each data but also each cluster. Using the new concept, we can consider the influence of clusters to each data by the tolerance. First, the new concept of tolerance is introduced into optimizat
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Hwang, Jeongsik, and Sadaaki Miyamoto. "Kernel Functions Derived from Fuzzy Clustering and Their Application to Kernel Fuzzyc-Means." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 1 (2011): 90–94. http://dx.doi.org/10.20965/jaciii.2011.p0090.

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Among widely used kernel functions, such as support vector machines, in data analysis, the Gaussian kernel is most often used. This kernel arises in entropy-based fuzzyc-means clustering. There is reason, however, to check whether other types of functions used in fuzzyc-means are also kernels. Using completely monotone functions, we show they can be kernels if a regularization constant proposed by Ichihashi is introduced. We also show how these kernel functions are applied to kernel-based fuzzyc-means clustering, which outperform the Gaussian kernel in a typical example.
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Yamamoto, Takeshi, Katsuhiro Honda, Akira Notsu, and Hidetomo Ichihashi. "A Comparative Study on TIBA Imputation Methods in FCMdd-Based Linear Clustering with Relational Data." Advances in Fuzzy Systems 2011 (2011): 1–10. http://dx.doi.org/10.1155/2011/265170.

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Relational fuzzy clustering has been developed for extracting intrinsic cluster structures of relational data and was extended to a linear fuzzy clustering model based on Fuzzyc-Medoids (FCMdd) concept, in which Fuzzyc-Means-(FCM-) like iterative algorithm was performed by defining linear cluster prototypes using two representative medoids for each line prototype. In this paper, the FCMdd-type linear clustering model is further modified in order to handle incomplete data including missing values, and the applicability of several imputation methods is compared. In several numerical experiments,
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Kinoshita, Naohiko, and Yasunori Endo. "On Objective-Based Rough Hard and Fuzzyc-Means Clustering." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 1 (2015): 29–35. http://dx.doi.org/10.20965/jaciii.2015.p0029.

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Clustering is one of the most popular unsupervised classification methods. In this paper, we focus on rough clustering methods based on rough-set representation. Rough k-Means (RKM) is one of the rough clustering method proposed by Lingras et al. Outputs of many clustering algorithms, including RKM depend strongly on initial values, so we must evaluate the validity of outputs. In the case of objectivebased clustering algorithms, the objective function is handled as the measure. It is difficult, however to evaluate the output in RKM, which is not objective-based. To solve this problem, we propo
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Kanzawa, Yuchi. "A Maximizing Model of Spherical Bezdek-Type Fuzzy Multi-Medoids Clustering." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 6 (2015): 738–46. http://dx.doi.org/10.20965/jaciii.2015.p0738.

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This paper proposes three modifications for the maximizing model of spherical Bezdek-type fuzzyc-means clustering (msbFCM). First, we use multi-medoids instead of centroids (msbFMMdd), which is similar to modifying fuzzyc-means to fuzzy multi-medoids. Second, we kernelize msbFMMdd (K-msbFMMdd). msbFMMdd can only be applied to objects in the first quadrant of the unit hypersphere, whereas its kernelized form can be applied to a wider class of objects. The third modification is a spectral clustering approach to K-msbFMMdd using a certain assumption. This approach improves the local convergence p
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Honda, Katsuhiro, and Hidetomo Ichihashi. "A Regularization Approach to Fuzzy Clustering with Nonlinear Membership Weights." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 1 (2007): 28–34. http://dx.doi.org/10.20965/jaciii.2007.p0028.

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Fuzzyc-means (FCM) is the fuzzy version ofc-means clustering, in which memberships are fuzzified by introducing an additional parameter into the linear objective function of the weighted sum of distances between datapoints and cluster centers. Regularization of hardc-means clustering is another approach to fuzzification, in which regularization terms such as entropy and quadratic terms have been adopted. We generalized the fuzzification concept and propose a new approach to fuzzy clustering in which linear weights of hardc-means clustering are replaced by nonlinear ones through regularization.
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Zhang, Jian, and Ling Shen. "An Improved Fuzzyc-Means Clustering Algorithm Based on Shadowed Sets and PSO." Computational Intelligence and Neuroscience 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/368628.

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To organize the wide variety of data sets automatically and acquire accurate classification, this paper presents a modified fuzzyc-means algorithm (SP-FCM) based on particle swarm optimization (PSO) and shadowed sets to perform feature clustering. SP-FCM introduces the global search property of PSO to deal with the problem of premature convergence of conventional fuzzy clustering, utilizes vagueness balance property of shadowed sets to handle overlapping among clusters, and models uncertainty in class boundaries. This new method uses Xie-Beni index as cluster validity and automatically finds t
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Raj, A. Stanley, D. Hudson Oliver, and Y. Srinivas. "Geoelectrical Data Inversion by Clustering Techniques of Fuzzy Logic to Estimate the Subsurface Layer Model." International Journal of Geophysics 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/134834.

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Soft computing based geoelectrical data inversion differs from conventional computing in fixing the uncertainty problems. It is tractable, robust, efficient, and inexpensive. In this paper, fuzzy logic clustering methods are used in the inversion of geoelectrical resistivity data. In order to characterize the subsurface features of the earth one should rely on the true field oriented data validation. This paper supports the field data obtained from the published results and also plays a crucial role in making an interdisciplinary approach to solve complex problems. Three clustering algorithms
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Murata, Ryuichi, Yasunori Endo, Hideyuki Haruyama, and Sadaaki Miyamoto. "On Fuzzy c-Means for Data with Tolerance." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 5 (2006): 673–81. http://dx.doi.org/10.20965/jaciii.2006.p0673.

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This paper presents two new clustering algorithms which are based on the entropy regularized fuzzyc-means and can treat data with some errors. First, the tolerance is formulated and introduce into optimization problems of clustering. Next, the problems are solved using Kuhn-Tucker conditions. Last, the algorithms are constructed based on the results of solving the problems.
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Dissertations / Theses on the topic "Fuzzyc- means clustering"

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Parker, Jonathon Karl. "Accelerated Fuzzy Clustering." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4929.

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Clustering algorithms are a primary tool in data analysis, facilitating the discovery of groups and structure in unlabeled data. They are used in a wide variety of industries and applications. Despite their ubiquity, clustering algorithms have a flaw: they take an unacceptable amount of time to run as the number of data objects increases. The need to compensate for this flaw has led to the development of a large number of techniques intended to accelerate their performance. This need grows greater every day, as collections of unlabeled data grow larger and larger. How does one increase the spe
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Kanade, Parag M. "Fuzzy ants as a clustering concept." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000397.

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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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Naik, Vaibhav C. "Fuzzy C-means clustering approach to design a warehouse layout." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000437.

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FURUHASHI, Takeshi, and Makoto YASUDA. "Fuzzy Entropy Based Fuzzy c-Means Clustering with Deterministic and Simulated Annealing Methods." Institute of Electronics, Information and Communication Engineers, 2009. http://hdl.handle.net/2237/15060.

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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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Beca, Cofre Sebastián. "Clustering Difuso con Selección de Atributos." Tesis, Universidad de Chile, 2007. http://www.repositorio.uchile.cl/handle/2250/104686.

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Stetco, Adrian. "An investigation into fuzzy clustering quality and speed : fuzzy C-means with effective seeding." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/an-investigation-into-fuzzy-clustering-quality-and-speed-fuzzy-cmeans-with-effective-seeding(fac3eab2-919a-436c-ae9b-1109b11c1cc2).html.

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Cluster analysis, the automatic procedure by which large data sets can be split into similar groups of objects (clusters), has innumerable applications in a wide range of problem domains. Improvements in clustering quality (as captured by internal validation indexes) and speed (number of iterations until cost function convergence), the main focus of this work, have many desirable consequences. They can result, for example, in faster and more precise detection of illness onset based on symptoms or it could provide investors with a rapid detection and visualization of patterns in financial time
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Altinel, Fatih. "An Empirical Study On Fuzzy C-means Clustering For Turkish Banking System." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12615027/index.pdf.

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Banking sector is very sensitive to macroeconomic and political instabilities and they are prone to crises. Since banks are integrated with almost all of the economic agents and with other banks, these crises affect entire societies. Therefore, classification or rating of banks with respect to their credibility becomes important. In this study we examine different models for classification of banks. Choosing one of those models, fuzzy c-means clustering, banks are grouped into clusters using 48 different ratios which can be classified under capital, assets quality, liquidity, profitability, in
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Chakeri, Alireza. "Scalable Clustering Using the Dempster-Shafer Theory of Evidence." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6478.

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Clustering large data sets has become very important as the amount of available unlabeled data increases. Single Pass Fuzzy C-Means (SPFCM) is useful when memory is too limited to load the whole data set. The main idea is to divide dataset into several chunks and to apply fuzzy c-means (FCM) to each chunk. SPFCM uses the weighted cluster centers of the previous chunk in the next data chunks. However, when the number of chunks is increased, the algorithm shows sensitivity to the order in which the data is processed. Hence, we improved SPFCM by recognizing boundary and noisy data in each chunk a
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Books on the topic "Fuzzyc- means clustering"

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Miyamoto, Sadaaki. Algorithms for fuzzy clustering: Methods in c-means clustering with applications. Springer, 2008.

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Fatemi-Ghomi, N. Texture segmentation using wavelet packets and c-means fuzzy clustering. Dept. of Electronic and Electrical Engineering, 1995.

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Fitriyati, Nina. Penentuan karakteristik alumni UIN Syarif Hidayatullah Jakarta periode wisuda April 2008 dan Juli 2008 menggunakan metode fuzzy C-means clustering. Kerjasama Lembaga Penelitian UIN Jakarta dengan UIN Jakarta Press, 2008.

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Miyamoto, Sadaaki, Hidetomo Ichihashi, and Katsuhiro Honda. Algorithms for Fuzzy Clustering: Methods in c-Means Clustering with Applications. Springer, 2010.

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Fuzzy Clustering Means Algorithm for Track Fusion in U.S. Coast Guard Vessel Traffic Service Systems. Storming Media, 1999.

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Book chapters on the topic "Fuzzyc- means clustering"

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Kondalarao, B., S. Sahoo, and D. K. Pratihar. "Character Recognition Using Entropy-Based FuzzyC-Means Clustering." In Hybrid Intelligence for Image Analysis and Understanding. John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781119242963.ch2.

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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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Sreevalsan-Nair, Jaya. "Fuzzy C-Means Clustering." In Encyclopedia of Mathematical Geosciences. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-85040-1_129.

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Sreevalsan-Nair, Jaya. "Fuzzy C-Means Clustering." In Encyclopedia of Mathematical Geosciences. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-26050-7_129-1.

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Gayen, Souvik, and Animesh Biswas. "Pythagorean Fuzzy c-means Clustering Algorithm." In Communications in Computer and Information Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75529-4_10.

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Dang, Trong Hop, Xuan Hoang Nguyen, Van Manh Nguyen, Manh Hung Hoang, Long Giang Nguyen, and Dinh Sinh Mai. "Border Fuzzy C-Means Clustering Algorithm." In Communications in Computer and Information Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4288-5_15.

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Gopala Krishna, P., and D. Lalitha Bhaskari. "Fuzzy C-Means and Fuzzy TLBO for Fuzzy Clustering." In Advances in Intelligent Systems and Computing. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2517-1_46.

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Kanzawa, Yuchi. "Semi-supervised Fuzzy c-Means Algorithms by Revising Dissimilarity/Kernel Matrices." In Fuzzy Sets, Rough Sets, Multisets and Clustering. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-47557-8_4.

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Ceccarelli, M., and A. Maratea. "Semi-supervised Fuzzy c-Means Clustering of Biological Data." In Fuzzy Logic and Applications. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11676935_32.

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Kanzawa, Yuchi, Yasunori Endo, and Sadaaki Miyamoto. "Indefinite Kernel Fuzzy c-Means Clustering Algorithms." In Modeling Decisions for Artificial Intelligence. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16292-3_13.

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Conference papers on the topic "Fuzzyc- means clustering"

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Ibrahim, Omar A., Jianxi Wang, Marek Z. Reformat, Petr Musilek, and James C. Bezdek. "Generalized Deep Embedded Fuzzy C-Means for Clustering High-Dimensional Data." In 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2024. http://dx.doi.org/10.1109/fuzz-ieee60900.2024.10611833.

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Yang, Zhijing, Boyang Yan, Junjie Zheng, Yiding Tang, Chuan Qian, and Hui Zhang. "Individual Fairness for Fuzzy C-Means Clustering." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888003.

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Ouedrhiri, Oumayma, Usef Faghihi, Fadel Toure, and Oumayma Banouar. "Quantum Fidelity Based Fuzzy C-Means Clustering Algorithm." In 2024 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2024. https://doi.org/10.1109/qce60285.2024.10267.

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Runkler, Thomas A., and James M. Keller. "Sequential possibilistic one-means clustering." In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015413.

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Hadjahmadi, A. H., M. M. Homayounpour, and S. M. Ahadi. "Robust weighted fuzzy c-means clustering." In 2008 IEEE 16th International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2008. http://dx.doi.org/10.1109/fuzzy.2008.4630382.

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Yasunori, Endo, Hamasuna Yukihiro, Yamashiro Makito, and Miyamoto Sadaaki. "On semi-supervised fuzzy c-means clustering." In 2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2009. http://dx.doi.org/10.1109/fuzzy.2009.5277177.

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Yeganejou, Mojtaba, and Scott Dick. "Classification via Deep Fuzzy c-Means Clustering." In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2018. http://dx.doi.org/10.1109/fuzz-ieee.2018.8491461.

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Runkler, Thomas A., and James M. Keller. "Fuzzy approaches to hard c-means clustering." In 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2012. http://dx.doi.org/10.1109/fuzz-ieee.2012.6251343.

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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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Szilagyi, Laszlo, Sandor M. Szilagyi, and Zoltan Benyo. "A unified approach to c-means clustering models." In 2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2009. http://dx.doi.org/10.1109/fuzzy.2009.5277132.

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Reports on the topic "Fuzzyc- means clustering"

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Kersten, P. R. Fuzzy Robust Statistics for Application to the Fuzzy c-Means Clustering Algorithm. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada274719.

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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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