Bibliografías temáticas / Fuzyy C-Means

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Literatura académica sobre el tema "Fuzyy C-Means"

Autor: Grafiati
Publicado: 7 de junio de 2025
Última modificación: 2 de agosto de 2025

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Artículos de revistas sobre el tema "Fuzyy C-Means"

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Kara, Mehmet Akif, and Dilayla Bayyurt. "AB Üyesi ve AB Üyeliğine Aday Ülkelerin Sağlık Göstergelerine Göre Bulanık C-Ortalamalar ve Bulanık C-Medoids Yöntemleri ile Kümelenmesi." Turkish Journal of Statistics and Data Science 01, no. 1 (2025): 54–62. https://doi.org/10.5281/zenodo.15364774.

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Siti, Lailiyah, Yulsilviana Ekawati, and Andrea Reza. "Clustering analysis of learning style on anggana high school student." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 3 (2019): 1409–16. https://doi.org/10.12928/TELKOMNIKA.v17i3.9101.

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The inability of students to absorb the knowledge conveyed by the teacher is’nt caused by the inability of understanding and by the teacher which isn’t able to teach too, but because of the mismatch of learning styles between students and teachers, so that students feel uncomfortable in learning to a particular teacher. It also happens in senior high school (SHS/SMAN) 1 Anggana, so it is necessary to do this research, to analyze cluster (group) of student learning style by applying data mining method that is k-Means and Fuzzy C-Means. The purpose was to know the effectiveness of th
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Ko, Jeong-Won, Byung-In Choi, and Frank Chung-Hoon Rhee. "A Density Estimation based Fuzzy C-means Algorithm for Image Segmentation." Journal of Korean Institute of Intelligent Systems 17, no. 2 (2007): 196–201. http://dx.doi.org/10.5391/jkiis.2007.17.2.196.

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Lee, Kwang-Kyu, and Jung-Hyun Woo. "A Study of Fuzzy C-Means Clustering Noise Processing Method." Journal of Korean Institute of Communications and Information Sciences 44, no. 1 (2019): 124–29. http://dx.doi.org/10.7840/kics.2019.44.1.124.

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Zuherman, Rustam, Purwanto Aldi, Hartini Sri, and Stephani Saragih Glori. "Lung cancer classification using fuzzy c-means and fuzzy kernel C-Means based on CT scan image." International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (2021): 291–97. https://doi.org/10.11591/ijai.v10.i2.pp291-297.

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Cancer is one of the diseases with the highest mortality rate in the world. Cancer is a disease when abnormal cells grow out of control that can attack the body's organs side by side or spread to other organs. Lung cancer is a condition when malignant cells form in the lungs. To diagnose lung cancer can be done by taking x-ray images, CT scans, and lung tissue biopsy. In this modern era, technology is expected to help research in the field of health. Therefore, in this study feature extraction from CT images was used as data to classify lung cancer. We used CT scan image data from SPIE-AAP
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OHTA, Tomohiro, Muneki NEMOTO, Hidetomo ICHIHASHI, and Tetsuya MIYOSHI. "Hard Clustering by Fuzzy c-Means." Journal of Japan Society for Fuzzy Theory and Systems 10, no. 3 (1998): 532–40. http://dx.doi.org/10.3156/jfuzzy.10.3_532.

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Lee, Jung-Ho, Won-Woo Lee, Joong-Hoon Kim, and Hwan-Don Jun. "Estimation of Urban Inundation Risk using Fuzzy C-Means." Journal of Korean Society of Hazard Mitigation 11, no. 4 (2011): 229–35. http://dx.doi.org/10.9798/kosham.2011.11.4.229.

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Falidazia, Hasanah Faizana, Kartikasari Puspita, and Fakhriyana Deby. "Implementation of Fuzzy Possibilistic C-Means with Optimal Membership of Fuzzy C-Means for Stunting Management in Central Java Province." International Journal of Mathematics and Computer Research 13, no. 05 (2025): 5227–33. https://doi.org/10.5281/zenodo.15524233.

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Stunting is a serious problem makes children vulnerable disease and reduced productivity. According to Indonesian Health Survey (2023), stunting rate Indonesia in 2023 was 21.5%. The target set in 2020-2024 National Medium-Term Development Plan of 14% and WHO standard below 20% have still not been achieved. Based on Indonesian Health Survey (2023), prevalence stunting in Central Java Province in 2023 has decreased only 0.1% to 20.7%. Therefore, it is necessary to evaluate acceleration stunting handling from achievement more focused and targeted Special Index for Stunting Management (IKPS) indi
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Stetco, Adrian, Xiao-Jun Zeng, and John Keane. "Fuzzy C-means++: Fuzzy C-means with effective seeding initialization." Expert Systems with Applications 42, no. 21 (2015): 7541–48. http://dx.doi.org/10.1016/j.eswa.2015.05.014.

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Kusnadi, Adhi, and Abi Kabisah Maulillah. "Perbandingan Algoritma C-Means Clustering dan Fuzzy C-Means Clustering." Ultima Computing : Jurnal Sistem Komputer 11, no. 1 (2019): 51–54. http://dx.doi.org/10.31937/sk.v11i1.953.

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Salah satu operasi di dalam analisis citra adalah segmentasi citra. Pada mulanya proses segmentasi dilakukan untuk memisahkan objek dari latar belakangnya, sehingga segmentasi merupakan bagian penting dalam pengenalan objek. Saat ini segmentasi sudah mengalami perkembangan yang sangat pesat, bukan hanya untuk tujuan pengenalan objek saja tetapi juga untuk persoalan interpretasi citra, yaitu untuk mengetahui objek-objek yang termuat dalam suatu citra. Banyak algoritma sudah dikembangkan untuk proses segmentasi citra. Beberapa di antaranya adalah algoritma C-Means Clustering dan Fuzzy C-Means Cl
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Tesis sobre el tema "Fuzyy C-Means"

1

Dagher, Issam J. "Gradient based fuzzy c-means algorithm." FIU Digital Commons, 1994. http://digitalcommons.fiu.edu/etd/2652.

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A clustering algorithm based on the Fuzzy c-means algorithm (FCM) and the gradient descent method is presented. In the FCM, the minimization process of the objective function is proceeded by solving two equations alternatively in an iterative fashion. Each iteration requires the use of all the data at once. In our proposed approach one datum is presented at a time to the network and the minimization is proceeded using the gradient descent method. Compared to FCM, the experimental results show that our algorithm is very competitive in terms of speed and stability of convergence for large number
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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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GOMES, Evanice Pinheiro. "Regionalização de precipitações via fuzzy C-means." Universidade Federal do Pará, 2017. http://repositorio.ufpa.br/jspui/handle/2011/9833.

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Submitted by Ivone Costa (mivone@ufpa.br) on 2018-05-10T18:17:48Z No. of bitstreams: 1 Dissertação_RegionalizaçãoPrecipitaçãoVia.pdf: 7125170 bytes, checksum: eb1d1f2c67d1267a6b6463e2b6cc551f (MD5)<br>Approved for entry into archive by Ivone Costa (mivone@ufpa.br) on 2018-05-10T18:20:54Z (GMT) No. of bitstreams: 1 Dissertação_RegionalizaçãoPrecipitaçãoVia.pdf: 7125170 bytes, checksum: eb1d1f2c67d1267a6b6463e2b6cc551f (MD5)<br>Made available in DSpace on 2018-05-10T18:20:55Z (GMT). No. of bitstreams: 1 Dissertação_RegionalizaçãoPrecipitaçãoVia.pdf: 7125170 bytes, checksum: eb1d1f2c67d1267a6
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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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Khmag, Abdulhakim Emhemad. "Fuzzy land cover change detection and validation : a comparison of fuzzy and Boolean analyses in Tripoli City, Libya." Thesis, University of Leicester, 2013. http://hdl.handle.net/2381/27811.

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This research extends fuzzy methods to consider the fuzzy validation of fuzzy land cover data at the sub-pixel level. The study analyses the relationships between fuzzy memberships generated by field survey and those generated from the classification of remotely sensed data. In so doing it examines the variations in the relationship between observed and predicted fuzzy land cover classes. This research applies three land cover classification techniques: Fuzzy sets, Fuzzy c-means and Boolean classification, and develops three models to determine fuzzy land cover change. The first model is depen
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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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Milagre, Selma Terezinha. "Análise do número de grupos em bases de dados incompletas utilizando agrupamentos nebulosos e reamostragem Bootstrap." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/18/18153/tde-04032009-150315/.

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A técnica de agrupamento de dados é amplamente utilizada em análise exploratória, a qual é frequentemente necessária em diversas áreas de pesquisa tais como medicina, biologia e estatística, para avaliar potenciais hipóteses a serem utilizadas em estudos subseqüentes. Em bases de dados reais, a ocorrência de dados incompletos, nos quais os valores de um ou mais atributos do dado são desconhecidos, é bastante comum. Este trabalho apresenta um método capaz de identificar o número de grupos presentes em bases de dados incompletas, utilizando a combinação das técnicas de agrupamentos nebulosos e r
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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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PIMENTEL, Bruno Almeida. "Métodos de agrupamento difuso multivariado baseados no Fuzzy C-means." Universidade Federal de Pernambuco, 2017. https://repositorio.ufpe.br/handle/123456789/27506.

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Submitted by Fernanda Rodrigues de Lima (fernanda.rlima@ufpe.br) on 2018-10-05T21:21:36Z No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) TESE Bruno Almeida Pimentel.pdf: 1823660 bytes, checksum: 2dda6d26c679f00a5e642b456730f356 (MD5)<br>Approved for entry into archive by Alice Araujo (alice.caraujo@ufpe.br) on 2018-11-14T20:59:03Z (GMT) No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) TESE Bruno Almeida Pimentel.pdf: 1823660 bytes, checksum: 2dda6d26c679f00a5e642b456730f356 (MD5)<br>Made available in DSp
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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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Libros sobre el tema "Fuzyy C-Means"

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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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Capítulos de libros sobre el tema "Fuzyy C-Means"

1

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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Krumnikl, Michal, Eduard Sojka, and Jan Gaura. "Fuzzy C-Means Stereo Segmentation." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25530-9_3.

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Havens, Timothy C., James C. Bezdek, and Marimuthu Palaniswami. "Incremental Kernel Fuzzy c-Means." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27534-0_1.

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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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Bhargava, Rohan, and Balakrushna Tripathy. "Kernel Based Rough-Fuzzy C-Means." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-45062-4_20.

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Aktas, Nihal, and Selcuk Cebi. "Fraud Detection Using Fuzzy C Means." 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_11.

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Domingo-Ferrer, Josep, and Vicenç Torra. "Towards Fuzzy c-means Based Microaggregation." In Advances in Intelligent and Soft Computing. Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1773-7_29.

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Modenesi, Marta V., Myrian C. A. Costa, Alexandre G. Evsukoff, and Nelson F. F. Ebecken. "Parallel Fuzzy c-Means Cluster Analysis." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71351-7_5.

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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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Actas de conferencias sobre el tema "Fuzyy C-Means"

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He, YuAn, Kensuke Tanioka, Satoru Hiwa, and Tomoyuki Hiroyasu. "Sparse Projected Maximum Entropy Fuzzy c-Means." In 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2024. http://dx.doi.org/10.1109/fuzz-ieee60900.2024.10611813.

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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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Hore, P., L. O. Hall, D. B. Goldgof, and W. Cheng. "Online fuzzy c means." In NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society. IEEE, 2008. http://dx.doi.org/10.1109/nafips.2008.4531233.

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Hore, Prodip, Lawrence O. Hall, and Dmitry B. Goldgof. "Single Pass Fuzzy C Means." In 2007 IEEE International Fuzzy Systems Conference. IEEE, 2007. http://dx.doi.org/10.1109/fuzzy.2007.4295372.

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Chovancova, Olga, Lucia Piatrikova, and Adam Dudas. "Improving Fuzzy C-Means Algorithm using Particle Swarm Optimization and Fuzzy C-Means++." In 2019 International Conference on Information and Digital Technologies (IDT). IEEE, 2019. http://dx.doi.org/10.1109/dt.2019.8813680.

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Zhou, Huiyu, Gerald Schaefer, Abdul Sadka, and M. Emre Celebi. "Anisotropic mean shift based fuzzy c-means segmentation of skin lesions." In the 5th international conference. ACM Press, 2008. http://dx.doi.org/10.1145/1456223.1456313.

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Huiyu Zhou, Gerald Schaefer, and Chunmei Shi. "A mean shift based fuzzy c-means algorithm for image segmentation." In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2008. http://dx.doi.org/10.1109/iembs.2008.4649857.

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Mallick, Anup Kumar, and Achintya Das. "Improved fuzzy c-means clustering through ensemble single mean supervised learning." In TRANSPORT, ECOLOGY, SUSTAINABLE DEVELOPMENT: EKO VARNA 2023. AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0191600.

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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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Ichihashi, H., K. Nagaura, A. Notsu, and K. Honda. "Benchmarking parameterized fuzzy c-Means classifier." In 2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2009. http://dx.doi.org/10.1109/fuzzy.2009.5277059.

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Informes sobre el tema "Fuzyy C-Means"

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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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