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Journal articles on the topic 'Fuzzy C-Medoids'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

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> This work is to propose a more effective Fuzzy C-means clustering algorithm for predicting student performance based on their health. <strong>Methods:</strong> 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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Yamamoto, Takeshi, Katsuhiro Honda, Akira Notsu, and Hidetomo Ichihashi. "Non-Euclidean Extension of FCMdd-Based Linear Clustering for Relational Data." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 8 (2011): 1050–56. http://dx.doi.org/10.20965/jaciii.2011.p1050.

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Relational data is common in many real-world applications. Linear fuzzy clustering models have been extended for handling relational data based on Fuzzyc-Medoids (FCMdd) framework. In this paper, with the goal being to handle non-Euclidean data, β-spread transformation of relational data matrices used in Non-Euclidean-type Relational Fuzzy (NERF)c-means is applied before FCMdd-type linear cluster extraction. β-spread transformation modifies data elements to avoid negative values for clustering criteria of distances between objects and linear prototypes. In numerical experiments, typical featur

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Indah Ratih Anggriyani, Dariani Matualage, and Esther Ria Matulessy. "KAJIAN METODE FUZZY K-RATAAN DAN FUZZY K-MEDOIDS (STUDI KASUS: PENGELOMPOKAN DESA DI KABUPATEN SORONG TAHUN 2016 BERDASARKAN STATUS KETERTINGGALAN)." Jurnal Natural 14, no. 1 (2020): 1–13. http://dx.doi.org/10.30862/jn.v14i1.3.

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The developments research in the cluster analysis using the fuzzy method. The fuzzy method allocates to each group with membership value located at interval [0, 1], showing the magnitude of the possibility of an object being a member into a particular group. Outlier in data very important known before grouping, because affect the final result. Grouping by using the mean value as the center of the group will be more sensitive than using the median value, so this research applies fuzzy c-means and fuzzy c-medoid method to the grouping of villages in Sorong Regency Year 2016 based on the underdev

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HAGA, Naoki, Katsuhiro HONDA, Hidetomo ICHIHASHI, and Akira NOTSU. "Linear Clustering by Extended Fuzzy c-Medoids and Subspace Learning from Relational Data." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 21, no. 1 (2009): 151–59. http://dx.doi.org/10.3156/jsoft.21.151.

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Dwitiyanti, Nurfidah, Siti Ayu Kumala, and Shinta Dwi Handayani. "Comparative Study of Earthquake Clustering in Indonesia Using K-Medoids, K-Means, DBSCAN, Fuzzy C-Means and K-AP Algorithms." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 8, no. 6 (2024): 768–78. https://doi.org/10.29207/resti.v8i6.5514.

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Indonesia’s frequent earthquakes, caused by its position at the convergence of multiple tectonic plates, Indonesia's frequent earthquakes, caused by its position at the convergence of multiple tectonic plates, necessitate precise seismic zone identification to improve disaster preparedness. This research evaluates the effectiveness of five clustering algorithms—K-Medoids, K-Means, DBSCAN, Fuzzy C-Means, and K-Affinity Propagation (K-AP)—for analyzing earthquake data from January 2017 to January 2023. Using a dataset from BMKG encompassing 13,860 seismic events, each algorithm was assessed base

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Fauzan, Reyhan Muhammad, and Ganjar Alfian. "Segmentasi Pelanggan E-Commerce Menggunakan Fitur Recency, Frequency, Monetary (RFM) dan Algoritma Klasterisasi K-Means." JISKA (Jurnal Informatika Sunan Kalijaga) 9, no. 3 (2024): 170–77. http://dx.doi.org/10.14421/jiska.2024.9.3.170-177.

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The rapid growth in the e-commerce industry demands the development of smarter and more focused marketing strategies. One approach that can be applied is customer segmentation using various features such as Recency, Frequency, and Monetary (RFM), along with machine learning-based clustering methods. The objective of this study is to design and develop a web-based e-commerce customer segmentation application using a combination of RFM features and clustering methods. The study proposes the K-Means algorithm and compares it with K-Medoids and Fuzzy C Means using publicly available e-commerce dat

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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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Xu, Jiucheng, Qinchen Hou, Kanglin Qu, Yuanhao Sun, and Xiangru Meng. "A Fast Weighted Fuzzy C-Medoids Clustering for Time Series Data Based on P-Splines." Sensors 22, no. 16 (2022): 6163. http://dx.doi.org/10.3390/s22166163.

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The rapid growth of digital information has produced massive amounts of time series data on rich features and most time series data are noisy and contain some outlier samples, which leads to a decline in the clustering effect. To efficiently discover the hidden statistical information about the data, a fast weighted fuzzy C-medoids clustering algorithm based on P-splines (PS-WFCMdd) is proposed for time series datasets in this study. Specifically, the P-spline method is used to fit the functional data related to the original time series data, and the obtained smooth-fitting data is used as the

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Liu, Yongli, Jingli Chen, Shuai Wu, Zhizhong Liu, and Hao Chao. "Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance." PLOS ONE 13, no. 5 (2018): e0197499. http://dx.doi.org/10.1371/journal.pone.0197499.

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Annisa Nadaa Shabrina, M. Afdal, and Siti Monalisa. "Comparison Of K-Means, K-Medoids, and Fuzzy C-Means Algorithms for Clustering Drug User’s Addiction Levels." Jurnal Sistem Cerdas 6, no. 2 (2023): 113–22. http://dx.doi.org/10.37396/jsc.v6i2.313.

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Narcotics, psychotropics, and addictive substances are drugs that can activate brain systems, affect dopamine levels, and cause addiction. In Indonesia, there is a law requiring drug addicts to receive treatment and care. To properly treat a drug addict, it is first necessary to determine the level of addiction. Data mining methods such as clustering can be used to assess a user's level of drug addiction. This study uses the clustering algorithms Fuzzy C-means, K-Medoids, and K-means. The performance of the three clustering algorithms will then be evaluated based on the average similarity of c

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Khoo-Lattimore, Catheryn, Girish Prayag, and Marta Disegna. "Me, My Girls, and the Ideal Hotel: Segmenting Motivations of the Girlfriend Getaway Market Using Fuzzy C-Medoids for Fuzzy Data." Journal of Travel Research 58, no. 5 (2018): 774–92. http://dx.doi.org/10.1177/0047287518778154.

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Segmenting the motivation of travelers using the push and pull framework remains ubiquitous in tourism. This study segments the girlfriend getaway (GGA) market on motivation (push) and accommodation (pull) attributes and identifies relationships between these factors. Using a relatively novel clustering algorithm, the Fuzzy C-Medoids clustering for fuzzy data (FCM-FD), on a sample of 749 women travelers, three segments (Socializers, Enjoyers, and Rejoicers) are uncovered. The results of a multinomial fractional model show relationships between the clusters of motivation and accommodation attri

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Nuraeni, Fitri, Dewi Tresnawati, Yoga Handoko Agustin, and Gisna Fauzi. "OPTIMIZATION OF MARKET BASKET ANALYSIS USING CENTROID-BASED CLUSTERING ALGORITHM AND FP-GROWTH ALGORITHM." Jurnal Teknik Informatika (Jutif) 3, no. 6 (2022): 1581–90. http://dx.doi.org/10.20884/1.jutif.2022.3.6.399.

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The proliferation of the food and beverage sales business requires the creativity of business owners to offer their flagship products to every consumer, both new and subscribed consumers. A large number of menu choices makes the ordering process long because consumers are confused about which menu will be the best choice. the seller to be able to provide the right recommendations so that orders can take place faster. Shopping cart analysis is an activity that has often been done to find out the items found that are sold simultaneously. The FP-Growth association method is a faster algorithm for

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Maji, Pradipta, and Sankar K. Pal. "Rough-Fuzzy C-Medoids Algorithm and Selection of Bio-Basis for Amino Acid Sequence Analysis." IEEE Transactions on Knowledge and Data Engineering 19, no. 6 (2007): 859–72. http://dx.doi.org/10.1109/tkde.2007.190609.

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Durbenov, Raiymbek, Zhanar Lamasheva, Valerii Lakhno, and Makpal Zhartybayeva. "CLUSTERING IN COMPILING INVESTMENT PORTFOLIOS." Вестник КазАТК 132, no. 3 (2024): 206–15. http://dx.doi.org/10.52167/1609-1817-2024-132-3-206-215.

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Clustering is an effective tool for diversifying investments, reducing risk and identifying new opportunities. Clustering plays a key role in data analysis, allowing you to group objects with similar characteristics. This study examines the application of clustering to the tasks of forming and optimizing investment portfolios. The paper presents two clustering methods: K-medoids and fuzzy clustering (C-means). K-medoids divides assets into clusters by correlation, and C-means allows assets to belong to several clusters with varying degrees. The article analyzes various clustering methods in th

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Bei, Honghan, Yingchao Mao, Wenyang Wang, and Xu Zhang. "Fuzzy Clustering Method Based on Improved Weighted Distance." Mathematical Problems in Engineering 2021 (March 6, 2021): 1–11. http://dx.doi.org/10.1155/2021/6687202.

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As an essential data processing technology, cluster analysis has been widely used in various fields. In clustering, it is necessary to select appropriate measures to evaluate the similarity in the data. In this paper, firstly, a cluster center selection method based on the grey relational degree is proposed to solve the problem of sensitivity in initial cluster center selection. Secondly, combining the advantages of Euclidean distance, DTW distance, and SPDTW distance, a weighted distance measurement based on three kinds of reach is proposed. Then, it is applied to Fuzzy C-MeDOIDS and Fuzzy C-

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Hrytsyk, Volodymyr, Anton Borkivskyi, and Taras Oliinyk. "Achieving the Best Symmetry by Finding the Optimal Clustering Filters for Specific Lighting Conditions." Symmetry 16, no. 9 (2024): 1247. http://dx.doi.org/10.3390/sym16091247.

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This article explores the efficiency of various clustering methods for image segmentation under different luminosity conditions. Image segmentation plays a crucial role in computer vision applications, and clustering algorithms are commonly used for this purpose. The search for an adaptive clustering mechanism aims to ensure the maximum symmetry of real objects with objects/segments in their digital representations. However, clustering method performances can fluctuate with varying lighting conditions during image capture. Therefore, we assess the performance of several clustering algorithms—i

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Rocha, Allana Lais dos Santos, Ester Deschamps De Macêdo, Letícia Castro Portela De Oliveira, and Vinícius Ferreira Silva. "Aplicação de Clustering para Segmentação de Clientes na Base de Dados da JUSTA." Revista de Engenharia e Pesquisa Aplicada 7, no. 3 (2022): 39–53. http://dx.doi.org/10.25286/repa.v7i3.2458.

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Empresas de tecnologia financeira, mais conhecidas como fintechs, são companhias de inovação tecnológica com potencial transformador para o setor financial. Nelas, o tratamento personalizado requer a análise de quantidades expressivas de dados. Dessa forma, utilizar técnicas de mineração de dados pode oferecer maior facilidade em classificar e visualizar os consumidores. A empresa analisada nesse artigo, a Justa, é uma fintech que promove produtos e serviços através de uma conta digital, e que procurava aprimorar a classificação dos seus clientes. A partir das bases de dados anonimizadas, forn

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Manoranjani, M. "Performance of ML-Based Unsupervised Clustering Algorithms for WSN Node Clustering." International Journal for Research in Applied Science and Engineering Technology 12, no. 1 (2024): 153–58. http://dx.doi.org/10.22214/ijraset.2024.57868.

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Abstract: Wireless Sensor Networks are generally deployed in dynamically changing environment. When compared to common wired network nodes, WSN nodes must do more work. Since WSN devices are battery powered, so power management is a challenge. Clustering is one solution that has been proposed to alleviate the issue of limited power. Clustering is the most important method stabilizes the lifetime of the network. It entails the aggregation of sensor nodes into clusters and cluster head is picked out from all the clusters. Clustering is implemented in wireless sensor networks through the Machine

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OZDEMIR, O., and A. KAYA. "K-MEDOIDS AND FUZZY C-MEANS ALGORITHMS FOR CLUSTERING CO2 EMISSIONS OF TURKEY AND OTHER OECD COUNTRIES." Applied Ecology and Environmental Research 16, no. 3 (2018): 2513–26. http://dx.doi.org/10.15666/aeer/1603_25132526.

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Karputri, Diah Leni, and Wiyli Yustanti. "Analisis Klastering Buku sebagai Evaluasi untuk Peningkatan Minat Baca Perpusatakaan SMAN 1 Grogol." Journal of Emerging Information System and Business Intelligence (JEISBI) 3, no. 3 (2022): 94–101. https://doi.org/10.26740/jeisbi.v3i3.47121.

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SMAN 1 Grogol merupakan sekolah menengah atas yang berlokasi di Kabupaten Kediri. Berdasarkan hasil wawancara dan observasi yang dilakukan kepada staf perpustakaan, saat ini kondisi minat baca di SMAN 1 Grogol tergolong cukup rendah, hal ini dilihat dari data rekap peminjaman buku di perpustakaan selama lima tahun terakhir yang fluktuatif. Pada perpustakaan penempatan buku juga belum optimal, sehingga perpustakaan SMAN 1 Grogol berkeinginan untuk meningkatkan pelayanannya. Peningkatan pelayanan memberikan kemudahan pada siswa dalam mencari buku sesuai dengan minat baca dan meningkatkan rasa te

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Syahzaqi, Idrus, Magdalena Effendi, Hasri Rahmawati, Heri Kuswanto, and Sediono Sediono. "GROUPING PROVINCES IN INDONESIA BASED ON THE NUMBER OF VILLAGES AFFECTED BY ENVIROMENTAL POLLUTION WITH K-MEDOIDS, FUZZY C-MEANS, AND DBSCAN." BAREKENG: Jurnal Ilmu Matematika dan Terapan 18, no. 2 (2024): 0923–36. http://dx.doi.org/10.30598/barekengvol18iss2pp0923-0936.

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Pollution can cause the environment to not function properly and ultimately harm humans and other living things. Environmental pollution is a problem that needs to be resolved because it involves the safety, health, and survival of living things. Air pollution in Pekanbaru due to a long dry season has resulted in forest fires. Then, 70% of drinking water is contaminated by fecal waste. In addition, the contamination of the land by the Chevron company resulted in residents suing the company. Until now, there has been no research that has carried out a comparison between methods for grouping vil

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Guerreiro, Marcio Trindade, Eliana Maria Andriani Guerreiro, Tathiana Mikamura Barchi, et al. "Anomaly Detection in Automotive Industry Using Clustering Methods—A Case Study." Applied Sciences 11, no. 21 (2021): 9868. http://dx.doi.org/10.3390/app11219868.

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In automotive industries, pricing anomalies may occur for components of different products, despite their similar physical characteristics, which raises the total production cost of the company. However, detecting such discrepancies is often neglected since it is necessary to find the problems considering the observation of thousands of pieces, which often present inconsistencies when specified by the product engineering team. In this investigation, we propose a solution for a real case study. We use as strategy a set of clustering algorithms to group components by similarity: K-Means, K-Medoi

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Trojan, Flavio, Pablo Isaias Rojas Fernandez, Marcio Guerreiro, et al. "Class Thresholds Pre-Definition by Clustering Techniques for Applications of ELECTRE TRI Method." Energies 16, no. 4 (2023): 1936. http://dx.doi.org/10.3390/en16041936.

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The sorting problem in the Multi-criteria Decision Analysis (MCDA) has been used to address issues whose solutions involve the allocation of alternatives in classes. Traditional multi-criteria methods are commonly used for this task, such as ELECTRE TRI, AHP-Sort, UTADIS, PROMETHEE, GAYA, etc. While using these approaches to perform the sorting procedure, the decision-makers define profiles (thresholds) for classes to compare the alternatives within these profiles. However, most such applications are based on subjective tasks, i.e., decision-makers’ expertise, which sometimes might be imprecis

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Mat, Ayşe Nagehan, Onur İnan, and Murat Karakoyun. "An application of the whale optimization algorithm with Levy flight strategy for clustering of medical datasets." An International Journal of Optimization and Control: Theories & Applications (IJOCTA) 11, no. 2 (2021): 216–26. http://dx.doi.org/10.11121/ijocta.01.2021.001091.

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Clustering, which is handled by many researchers, is separating data into clusters without supervision. In clustering, the data are grouped using similarities or differences between them. Many traditional and heuristic algorithms are used in clustering problems and new techniques continue to be developed today. In this study, a new and effective clustering algorithm was developed by using the Whale Optimization Algorithm (WOA) and Levy flight (LF) strategy that imitates the hunting behavior of whales. With the developed WOA-LF algorithm, clustering was performed using ten medical datasets take

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Saidul, Achmad, and Joko Lianto Buliali. "Implementasi Particle Swarm Optimization pada K-Means untuk Clustering Data Automatic Dependent Surveillance-Broadcast." Eksplora Informatika 8, no. 1 (2018): 30. http://dx.doi.org/10.30864/eksplora.v8i1.150.

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Investigasi kecelakaan penerbangan di Indonesia pada tahun 2010 sampai 2016 sebesar 212 investigasi. Hal tersebut dapat dihindari apabila ada sistem penerbangan yang dapat memastikan penerbangan berjalan aman, seperti sistem lalu lintas udara yang dapat mendeteksi apabila pesawat bergerak menuju ke arah yang salah. Penelitian ini bertujuan untuk mengelompokkan rute penerbangan pada data Automatic Dependent Surveillance-Broadcast menggunakan metode clustering untuk mendapatkan similaritas rute penerbangan. Penulis mengusulkan metode particle swarm optimization untuk mengoptimalkan metode k-mean

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Shen, Ling, Jian Lu, Man Long, and Tingjun Chen. "Identification of Accident Blackspots on Rural Roads Using Grid Clustering and Principal Component Clustering." Mathematical Problems in Engineering 2019 (January 21, 2019): 1–12. http://dx.doi.org/10.1155/2019/2151284.

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Identifying road accident blackspots is an effective strategy for reducing accidents. The application of this method in rural areas is different from highway and urban roads as the latter two have complete geographic information. This paper presents (1) a novel segmentation method using grid clustering and K-MEDOIDS to study the spatial patterns of road accidents in rural roads, (2) a clustering methodology using principal component analysis (PCA) and improved K-means to create recognition of road accident blackspots based on segmented results, and (3) using accidents causes in police report t

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Prastanika, Winar Wahyu, and Arie Wahyu Wijayanto. "Analisis Hard dan Soft Clustering Untuk Pengelompokan Indikator Ketahanan Pangan Indonesia 2021." Jurnal Sistem dan Teknologi Informasi (JustIN) 11, no. 4 (2023): 596. http://dx.doi.org/10.26418/justin.v11i4.68400.

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Ketahanan pangan adalah suatu kondisi terpenuhinya pangan suatu negara atau perseorangan baik dari segi variasi, kuantitas , kualitas, keamanan, dan gizi. Ketahanan pangan menjadi isu penting yang harus diperhatikan akhir-akhir ini guna mewujudkan salah satu tujuan SDGs. Penelitian ini bertujuan untuk mengelompokkan provinsi-provinsi di Indonesia berdasarkan karakteristik indikator ketahanan pangan dan membandingkan beberapa metode untuk mendapatkan metode pengelompokan yang terbaik. Metode-metode yang dibandingkan yaitu hard clustering (K-Means dan K-Medoids) dan soft clustering (Fuzzy C-Mean

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Agustina Heryati, Terttiaavini Terttiaavini, Septa Cahyani, K.Ghazali, Harsi Romli, and Iski Zaliman. "E-Commerce Marketing Strategy Optimization Through Machine Learning-Based Conversion Prediction." JSAI (Journal Scientific and Applied Informatics) 8, no. 1 (2025): 66–73. https://doi.org/10.36085/jsai.v8i1.7553.

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The research identifies the problem of enhancing e-commerce sales conversion through TikTok amidst intense content competition. The objective of the study is to develop a machine learning-based marketing strategy to analyze user behavior and categorize them into Non-Purchasers and Purchasers.The method employed includes clustering using K-Means, K-Medoids, and Fuzzy C-Means algorithms, with K-Means demonstrating the best performance, achieving the highest Silhouette Coefficient (0.1857) and the lowest Davies-Bouldin Index (1.9991). Following clustering, classification is performed using Naïve

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Nur'aini, Risa, Tatik Widiharih, and Bagus Arya Saputra. "PENGELOMPOKAN KABUPATEN/KOTA DI PROVINSI JAWA TENGAH BERDASARKAN INDIKATOR KESEHATAN BAYI DAN BALITA MENGGUNAKAN ALGORITMA FUZZY C-MEANS DAN K-MEDOIDS." Jurnal Gaussian 13, no. 1 (2024): 189–98. http://dx.doi.org/10.14710/j.gauss.13.1.189-198.

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zolghadry, shaghayegh, Mehrdad Ghodskhah daryaei, Kamran Nasirahmadi, and Esmaei Ghajar. "Comparison of the Performance of Fuzzy C-Means and K-Medoids in Modeling Forest Fire Occurrence (Case Study: Saravan Forests, Gilan)." Ecology of Iranian Forests 9, no. 17 (2021): 163–74. http://dx.doi.org/10.52547/ifej.9.17.163.

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J. Serin, J. Serin, J. SatheeshKumar J. Serin, and T. Amudha J. SatheeshKumar. "Efficient Fuzzy C-means Based Reduced Feature Set Association Rule Mining Approach for Predicting the User Behavioral Pattern in Web Usage Mining." 網際網路技術學刊 23, no. 7 (2022): 1495–503. http://dx.doi.org/10.53106/160792642022122307005.

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<p>Online business and marketing are becoming popular now a day due to the wide variety of products available from multiple vendors in online. One of the major challenges of e-business merchants is predicting the buying and selling patterns of online customers. Global level competition is another challenge faced by online merchants due to the lowest prices and offers provided by multiple sellers for the same or similar product. Hence, the development of an efficient web mining framework to analyze and predict buyer&rsquo;s interest based on the browsing history will be a great suppor

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Gubu, La, Edi Cahyono, Arman Arman, Herdi Budiman, and Muh Kabil Djafar. "Family of K-Means Clustering for Robust Mean-Variance Portfolio Selection: A Comparison of K-Medoids, K-Means, and Fuzzy C-Means." Industrial Engineering & Management Systems 23, no. 3 (2024): 342–56. http://dx.doi.org/10.7232/iems.2024.23.3.342.

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Br Bangun, Desy Milbina, Syahril Efendi, and Rahmat W. Sembiring. "Analysis of Data classification accuracy using ANFIS algorithm modification with K-Medoids clustering." SinkrOn 7, no. 3 (2022): 2080–88. http://dx.doi.org/10.33395/sinkron.v7i3.11610.

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The ANFIS algorithm is a technique in data mining that can be used for the data classification process. The ANFIS algorithm still has weaknesses, especially in determining the initial parameters for the network training process. Thus, an additional algorithm or modification is needed for the determination of these parameters. In this study, a clustering method will be proposed, namely K-Medoids Clustering as an additional method to the ANFIS algorithm. Basically, the ANFIS algorithm uses the FCM (Fuzzy C-Means Clustering) algorithm for the initial initialization of network parameters. The use

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Ningsih, I. Kadek Mira Merta, and Arie Wahyu Wijayanto. "Analisis Cluster Provinsi di Indonesia Berdasarkan Pertumbuhan Ekonomi Tahun 2022." Komputika : Jurnal Sistem Komputer 13, no. 1 (2024): 103–12. http://dx.doi.org/10.34010/komputika.v13i1.10520.

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Pembangunan ekonomi adalah agenda sentral yang bertujuan untuk mengembangkan perekonomian suatu negara secara berkelanjutan. Perekonomian Indonesia tahun 2022 tumbuh sebesar 5,31 persen, lebih tinggi dibanding capaian tahun 2021. Oleh sebab itu, mengingat ekonomi merupakan sektor yang sangat krusial maka pemerataan pertumbuhan ekonomi menjadi suatu hal penting untuk diperhatikan demi pemerataan kesejahteraan masyarakat Indonesia. Peneliti melakukan analisis terkait pengelompokan kondisi pertumbuhan ekonomi provinsi-provinsi di Indonesia tahun 2022 dengan menggunakan metode K-Means, K-Medoids,

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Cindy, Cindy, Cynthia Cynthia, Valentino Vito, Devvi Sarwinda, Bevina Desjwiandra Handari, and Gatot Fatwanto Hertono. "Cluster Analysis on Dengue Incidence and Weather Data Using K-Medoids and Fuzzy C-Means Clustering Algorithms (Case Study: Spread of Dengue in the DKI Jakarta Province)." Journal of Mathematical and Fundamental Sciences 53, no. 3 (2022): 466–86. http://dx.doi.org/10.5614/j.math.fund.sci.2021.53.3.9.

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In Indonesia, Dengue incidence tends to increase every year but has been fluctuating in recent years. The potential for Dengue outbreaks in DKI Jakarta, the capital city, deserves serious attention. Weather factors are suspected of being associated with the incidence of Dengue in Indonesia. This research used weather and Dengue incidence data for five regions of DKI Jakarta, Indonesia, from December 30, 2008, to January 2, 2017. The study used a clustering approach on time-series and non-time-series data using K-Medoids and Fuzzy C-Means Clustering. The clustering results for the non-time-seri

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Xu, Fan, Xin Shu, Xin Li, and Xiaodi Zhang. "Data-Driven Bearing Fault Diagnosis of Microgrid Network Power Device Based on a Stacked Denoising Autoencoder in Deep Learning and Clustering by Fast Search without Data Labels." Complexity 2020 (November 2, 2020): 1–29. http://dx.doi.org/10.1155/2020/5013871.

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The traditional health indicator (HI) construction method of electric equipment devices in microgrid networks, such as bearings that require different time-frequency domain indicators, needs several models to combine. Therefore, it is necessary to manually select appropriate and sensitive models, such as time-frequency domain indicators and multimodel fusion, to build HIs in multiple steps, which is more complicated because sensitivity characteristics and suitable models are more representatives of bearing degradation trends. In this paper, we use the stacked denoising autoencoder (SDAE) model

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Thamrin, Nurafiza, and Arie Wahyu Wijayanto. "Comparison of Soft and Hard Clustering: A Case Study on Welfare Level in Cities on Java Island." Indonesian Journal of Statistics and Its Applications 5, no. 1 (2021): 141–60. http://dx.doi.org/10.29244/ijsa.v5i1p141-160.

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The National Medium Term Development Plan 2020-2024 states that one of the visions of national development is to accelerate the distribution of welfare and justice. Cluster analysis is analysis that grouping of objects into several smaller groups where the objects in one group have similar characteristics. This study was conducted to find the best clustering method and to classify cities based on the level of welfare in Java. In this study, the cluster analysis that used was hard clustering such as K-Means, K-Medoids (PAM and CLARA), and Hierarchical Agglomerative as well as soft clustering su

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Vunnava, Dinesh Babu, and Malathi Karunakaran. "Large dataset partitioning using ensemble partition-based clustering with majority voting technique." Large dataset partitioning using ensemble partition-based clustering with majority voting technique 29, no. 2 (2023): 838–44. https://doi.org/10.11591/ijeecs.v29.i2.pp838-844.

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Large datasets have become useful in data mining for processing, storing, and handling vast amounts of data. However, handling and processing large datasets is time-consuming and memory intensive. As a result, the researchers adopted a partitioning strategy to improve controllability and performance and reduce the time and memory required to handle large datasets. Unfortunately, the numerous clustering techniques available in the literature could confuse experts in choosing the best techniques for a given dataset. Furthermore, no clustering technique can tackle all problems, such as cluster st

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Suraya, Ghina Rofifa, and Arie Wahyu Wijayanto. "Comparison of Hierarchical Clustering, K-Means, K-Medoids, and Fuzzy C-Means Methods in Grouping Provinces in Indonesia according to the Special Index for Handling Stunting." Indonesian Journal of Statistics and Its Applications 6, no. 2 (2022): 180–201. http://dx.doi.org/10.29244/ijsa.v6i2p180-201.

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Stunting has been widely known as the highest case of malnutrition suffered by toddlers in the world and has a bad impact on children's future. In 2018, Indonesia was ranked the 31st highest stunting in the world and ranked 4th in Southeast Asia. About 30.8% (roughly 3 out of 10) of children under 5 years suffer from stunting in Indonesia. To support the government policy making in handling stunting, it is undoubtedly necessary to classify the levels of stunting handling in regions in Indonesia. In this work, the hierarchical agglomerative and non-hierarchical clustering is compared and evalua

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Babu, Vunnava Dinesh, and Karunakaran Malathi. "Large dataset partitioning using ensemble partition-based clustering with majority voting technique." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 2 (2023): 838. http://dx.doi.org/10.11591/ijeecs.v29.i2.pp838-844.

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<span lang="EN-US">Large datasets have become useful in data mining for processing, storing, and handling vast amounts of data. However, handling and processing large datasets is time-consuming and memory intensive. As a result, the researchers adopted a partitioning strategy to improve controllability and performance and reduce the time and memory required to handle large datasets. Unfortunately, the numerous clustering techniques available in the literature could confuse experts in choosing the best techniques for a given dataset. Furthermore, no clustering technique can tackle all pro

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Prayoga, Suhendra Widi, and Setia Pramana. "Pemetaan Kejadian Balita Stunting Melalui Integrasi Citra Satelit Multisumber dan Official Statistics di Provinsi Nusa Tenggara Timur Menggunakan Machine Learning." PROSIDING SEMINAR NASIONAL SAINS DATA 4, no. 1 (2024): 434–44. https://doi.org/10.33005/senada.v4i1.245.

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Stunting is a serious problem for the health of children under five. The lack of quality of life for toddlers is one of the causes of the current high prevalence of stunting. East Nusa Tenggara ranks second with the highest prevalence of stunting in Indonesia in 2023. This high rate is triggered by several factors, such as health, socio-economics and environment. In terms of the environment, remote sensing technology can be utilised as a supporting tool in monitoring the incidence of stunting in the region. This study aims to map districts/cities in East Nusa Tenggara based on the incidence of

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