Bibliografías temáticas / Fuzzy C-Medoids

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Literatura académica sobre el tema "Fuzzy C-Medoids"

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 "Fuzzy C-Medoids"

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Sitepu, Epridonta, Kris Suryowati, and Noviana Pratiwi. "PENGELOMPOKAN KABUPATEN/ KOTA DI PROVINSI SUMATERA UTARA BERDASARKAN INDIKATOR INDEKS PEMBANGUNAN MANUSIA TAHUN 2022 DENGAN METODE FUZZY C-MEANS DAN K- MEDOIDS." Jurnal Statistika Industri dan Komputasi 9, no. 2 (2024): 1–10. http://dx.doi.org/10.34151/statistika.v9i2.4848.

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Pengembangan indikator pembangunan manusia menjadi hal yang penting dalam upaya mengukur dan memahami kemajuan sosial dan ekonomi suatu wilayah. Provinsi Sumatera Utara, sebagai salah satu wilayah penting di Indonesia, juga perlu melakukan analisis yang mendalam terkait dengan pembangunan manusia di tingkat Kabupaten/Kota. Penelitian ini bertujuan untuk mengelompokkan Kabupaten/Kota di Provinsi Sumatera Utara berdasarkan indikator pembangunan manusia menggunakan metode Fuzzy C-Means dan K-Medoids. Data yang digunakan dalam penelitian ini adalah data sekunder yang mencakup sejumlah indikator pe
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D׳Urso, Pierpaolo, and Jacek M. Leski. "Fuzzy c -ordered medoids clustering for interval-valued data." Pattern Recognition 58 (October 2016): 49–67. http://dx.doi.org/10.1016/j.patcog.2016.04.005.

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Haga, N., K. Honda, A. Notsu, and H. Ichihashi. "Local subspace learning by extended fuzzy c-medoids clustering." International Journal of Knowledge Engineering and Soft Data Paradigms 2, no. 2 (2010): 169. http://dx.doi.org/10.1504/ijkesdp.2010.034681.

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Glori, Stephani Saragih, Hartini Sri, and Rustam Zuherman. "Comparison between fuzzy kernel k-medoids using radial basis function kernel and polynomial kernel function in hepatitis classification." International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (2021): 60–65. https://doi.org/10.11591/ijai.v10.i1.pp60-65.

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This paper compares the fuzzy kernel k-medoids using radial basis function (RBF) and polynomial kernel function in hepatitis classification. These two kernel functions were chosen due to their popularity in any kernel-based machine learning method for solving the classification task. The hepatitis dataset then used to evaluate the performance of both methods that were expected to provide an accurate diagnosis in patients to obtain treatment at an early phase. The data were obtained from two hospitals in Indonesia, consisting of 89 hepatitis-B and 31 hepatitis-C samples. The data were analyzed
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Syukron, Hamdi, Muhammad Fauzi Fayyad, Farin Junita Fauzan, Yulia Ikhsani, and Umairah Rizkya Gurning. "Perbandingan K-Means K-Medoids dan Fuzzy C-Means untuk Pengelompokan Data Pelanggan dengan Model LRFM." MALCOM: Indonesian Journal of Machine Learning and Computer Science 2, no. 2 (2022): 76–83. http://dx.doi.org/10.57152/malcom.v2i2.442.

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Indonesia memiliki pasar yang potensial untuk perusahaan kosmetik karena memiliki jumlah penduduk yang berjumlah hampir 270 juta jiwa. Pertumbuhan industri kosmetik di Indonesia mengalami perkembangan yang pesat dengan persentase pertumbuhan 5,59% pada bulan agustus 2021 silam. Dengan pertumbuhan tersebut perusahaan kosmetik memiliki reseller yang tersebar diseluruh daerah Indonesia. Penelitian ini menggunakan data pelanggan dari salah satu reseller perusahaan kecantikan. Pelanggan mana yang sering berbelanja, produk mana yang sering mereka beli, dan klien mana yang paling setia adalah masalah
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Stephani Saragih, Glori, Sri Hartini, and Zuherman Rustam. "Comparison between fuzzy kernel k-medoids using radial basis function kernel and polynomial kernel function in hepatitis classification." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (2021): 60. http://dx.doi.org/10.11591/ijai.v10.i1.pp60-65.

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<span id="docs-internal-guid-10508d4e-7fff-5011-7a0e-441840e858c8"><span>This paper compares the fuzzy kernel k-medoids using radial basis function (RBF) and polynomial kernel function in hepatitis classification. These two kernel functions were chosen due to their popularity in any kernel-based machine learning method for solving the classification task. The hepatitis dataset then used to evaluate the performance of both methods that were expected to provide an accurate diagnosis in patients to obtain treatment at an early phase. The data were obtained from two hospitals in Indone
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Lubis, Andre Hasudungan, and Elysa Ramayana. "A Review on Appropriateness of Partitional Clustering Algorithms in Handling Transactional Data." International Journal of Research and Review 10, no. 9 (2023): 162–69. http://dx.doi.org/10.52403/ijrr.20230918.

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Clustering is an unsupervised learning that widely used in vast researches area. The technique also utilized in any disciplines that involves multivariate data analysis. In term of transactional data handling, the partitional clustering is promoted as the one method to explore knowledge from several attributes that are related the business. In this paper, we investigate the use of partitional clustering algorithms including k-means, k-medoids, Fuzzy C Means, CLARA, and CLARANS. The present article delineates the various stages that are integral to accomplishing a review. These stages encompass
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Sabrina, Syafa Marwa, and Tabah Heri Setiawan. "CLUSTERING ANALYSIS OF PROVINCIAL IN INDONESIA BASED ON THE 2023 HUMAN DEVELOPMENT INDEX INDICATORS USING THE K-MEDOIDS ALGORITHM." Jurnal Matematika UNAND 14, no. 1 (2025): 93. https://doi.org/10.25077/jmua.14.1.93-102.2025.

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Indonesia memiliki visi Indonesia Emas pada tahun 2045, namun pencapaian Indeks Pembangunan Manusia (IPM) dalam 20 tahun terakhir menunjukkan tantangan untuk mewujudkan visi tersebut. Penelitian ini menggunakan algoritma k-medoids untuk melakukan clustering provinsi di Indonesia berdasarkan indikator IPM tahun 2023. K-medoids dipilih karena keunggulannya dalam menangani outlier. Berdasarkan hasil perbandingan dengan metode k-means dan fuzzy c-means, metode k-medoids juga terbukti merupakan metode terbaik karena cluster yang terbentuk pada k-medoids terpisah dengan baik dan memiliki struktur ya
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Sihotang, Adela Satriwa, Rokhana Dwi Bekti, and Maria Titah Jatipaningrum. "PENGELOMPOKAN KABUPATEN/KOTA DI PROVINSI SUMATERA UTARA BERDASARKAN INDIKATOR JENIS KEKERASAN TERHADAP ANAK MENGGUNAKAN METODE K-MEDOIDS DAN FUZZY C-MEANS." Jurnal Statistika Industri dan Komputasi 9, no. 2 (2024): 11–19. http://dx.doi.org/10.34151/statistika.v9i2.4850.

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Kekerasan terhadap anak merupakan permasalahan yang sering terjadi di kalangan masyarakat, terutama di Provinsi Sumatera Utara. Menurut Dinas Pemberdayaan Perempuan dan Perlindungan Anak Provinsi Sumatera Utara, terjadi peningkatan tindak kekerasan terhadap anak dari tahun 2019 hingga 2021 berdasarkan kekerasan secara fisik, psikis, seksual, sosial dan penelantar. Penelitian ini bertujuan untuk menganalisis pola kekerasan dalam suatu kabupaten/kota melalui penerapan metode pengelompokan K-Medoids dan Fuzzy C-Means. Penggunaan metode tersebut dapat memberikan gambaran kepada pihak hukum maupun
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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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Tesis sobre el tema "Fuzzy C-Medoids"

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BRANCO, Diogo Philippini Pontual. "Agrupamento fuzzy c-medoids semi-supervisionado de dados relacionais representados por múltiplas matrizes de dissimilaridade." Universidade Federal de Pernambuco, 2017. https://repositorio.ufpe.br/handle/123456789/27527.

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Submitted by Pedro Barros (pedro.silvabarros@ufpe.br) on 2018-10-05T20:00:48Z No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) DISSERTAÇÃO Diogo Philippini Pontual Branco.pdf: 1129238 bytes, checksum: dfe9b5706880e9a9a4dd5acd39d83322 (MD5)<br>Approved for entry into archive by Alice Araujo (alice.caraujo@ufpe.br) on 2018-11-14T22:34:34Z (GMT) No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) DISSERTAÇÃO Diogo Philippini Pontual Branco.pdf: 1129238 bytes, checksum: dfe9b5706880e9a9a4dd5acd39d83322 (MD5)<br>
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Capítulos de libros sobre el tema "Fuzzy C-Medoids"

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Dogan, Onur, and Basar Oztaysi. "Gender Prediction from Classified Indoor Customer Paths by Fuzzy C-Medoids Clustering." In Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23756-1_21.

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Muda, Muhammad Adlansyah, Dedy Dwi Prastyo, and Muhammad Sjahid Akbar. "Clustering Stock Prices of Industrial and Consumer Sector Companies in Indonesia Using Fuzzy C-Means and Fuzzy C-Medoids Involving ACF and PACF." In Communications in Computer and Information Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0405-1_20.

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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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Király, András, Ágnes Vathy-Fogarassy, and János Abonyi. "Fuzzy c-Medoid Graph Clustering." In Artificial Intelligence and Soft Computing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07176-3_64.

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Coppi, R., P. D'Urso, and P. Giordani. "Fuzzy C-medoids clustering models for time-varying data." In Modern Information Processing. Elsevier, 2006. http://dx.doi.org/10.1016/b978-044452075-3/50017-0.

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Actas de conferencias sobre el tema "Fuzzy C-Medoids"

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Ferone, Alessio, and Antonio Maratea. "Decoy clustering through graded possibilistic c-medoids." In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015699.

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Labroche, Nicolas. "New incremental fuzzy c medoids clustering algorithms." In NAFIPS 2010 - 2010 Annual Meeting of the North American Fuzzy Information Processing Society. IEEE, 2010. http://dx.doi.org/10.1109/nafips.2010.5548263.

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Kaizu, Yousuke, Sadaaki Miyamoto, and Yasunori Endo. "Hard and Fuzzy c-Medoids for Asymmetric Networks." In 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (IFSA-EUSFLAT-15). Atlantis Press, 2015. http://dx.doi.org/10.2991/ifsa-eusflat-15.2015.63.

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Naoki Haga, Katsuhiro Honda, Hidetomo Ichihashi, and Akira Notsu. "Linear fuzzy clustering of relational data based on extended Fuzzy c-Medoids." In 2008 IEEE 16th International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2008. http://dx.doi.org/10.1109/fuzzy.2008.4630392.

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de Melo, Filipe M., and Francisco de A. T. de Carvalho. "Semi-supervised fuzzy c-medoids clustering algorithm with multiple prototype representation." In 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2013. http://dx.doi.org/10.1109/fuzz-ieee.2013.6622374.

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Cuong, Nguyen Anh, Dinh Sinh Mai, Do Viet Duc, Dang Trong Hop, Long Thanh Ngo, and Pham The Long. "Fuzzy C-Medoids Clustering Based on Interval Type-2 Inituitionistic Fuzzy Sets." In 2021 RIVF International Conference on Computing and Communication Technologies (RIVF). IEEE, 2021. http://dx.doi.org/10.1109/rivf51545.2021.9642067.

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Mukhopadhyay, Anirban, Ujjwal Maulik, and Sanghamitra Bandyopadhyay. "Hybrid evolutionary multiobjective fuzzy c-medoids clustering of categorical data." In 2013 IEEE Workshop on Hybrid Intelligent Models and Applications (HIMA). IEEE, 2013. http://dx.doi.org/10.1109/hima.2013.6615016.

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Carvalho, Francisco de A. T. de, Filipe M. de Melo, and Yves Lechevallier. "A Fuzzy C-Medoids Clustering Algorithm Based on Multiple Dissimilarity Matrices." In 2013 Brazilian Conference on Intelligent Systems (BRACIS). IEEE, 2013. http://dx.doi.org/10.1109/bracis.2013.26.

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Wang, Yingxu, Jiwen Dong, Jin Zhou, et al. "Fuzzy c-medoids method based on JS-divergence for uncertain data clustering." In 2017 4th International Conference on Information, Cybernetics and Computational Social Systems (ICCSS). IEEE, 2017. http://dx.doi.org/10.1109/iccss.2017.8091431.

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Biasetton, Nicolò, Pierpaolo D’Urso, Marta Disegna, and Luigi Salmaso. "Fuzzy C-Medoids Clustering Of CUB Data to Handle Likert-Type Scales Uncertainty." In 5th International Conference on Statistics: Theory and Applications (ICSTA 2023). Avestia Publishing, 2023. http://dx.doi.org/10.11159/icsta23.115.

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