Relevant bibliographies by topics / K-Medoid

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

Academic literature on the topic 'K-Medoid'

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

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Journal articles on the topic "K-Medoid"

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Solikhun, Muhammad Rahmansyah Siregar, Lise Pujiastuti, and Mochamad Wahyudi. "Manhattan Distance-based K-Medoids Clustering Improvement for Diagnosing Diabetic Disease." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 8, no. 6 (2024): 710–18. https://doi.org/10.29207/resti.v8i6.5894.

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Diabetes is a metabolic disorder characterized by blood glucose levels above normal limits. Diabetes occurs when the body is unable to produce sufficient insulin to regulate blood sugar levels. As a result, blood sugar management becomes impaired and there is no cure for diabetes. Early detection of diabetes provides an opportunity to delay or prevent its progression into acute stages. Clustering can help identify patterns and groups of diabetes symptoms by analyzing attributes that indicate these symptoms. In this study, researchers are using K-Medoid and Quantum K-Medoid methods for clusteri
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Kariyam, Abdurakhman, Subanar, Herni Utami, and Adhitya Ronnie Effendie. "Block-Based K-Medoids Partitioning Method with Standardized Data to Improve Clustering Accuracy." Mathematical Modelling of Engineering Problems 9, no. 6 (2022): 1613–21. http://dx.doi.org/10.18280/mmep.090622.

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Most of the existing k-medoid algorithms select the initial medoid randomly or use a specific formula based on the proximity matrix. This study proposes a block-based k-medoids partitioning method for clustering objects. To get the initial medoids, we search for an object representative from the block of the standard deviation and the sum of the variable values. We optimized the initial groups to update medoids, so this step can reduce the number of iterations to obtain partitioned data. The block-based k-medoids partitioning method applies to all types of data. To improve clustering accuracy,
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De Mathelin, Antoine, Nicolas Enrique Cecchi, François Deheeger, Mathilde Mougeot, and Nicolas Vayatis. "OneBatchPAM: A Fast and Frugal K-Medoids Algorithm." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 15 (2025): 16172–80. https://doi.org/10.1609/aaai.v39i15.33776.

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This paper proposes a novel k-medoids approximation algorithm to handle large-scale datasets with reasonable computational time and memory complexity. We develop a local-search algorithm that iteratively improves the medoid selection based on the estimation of the k-medoids objective. A single batch of size m
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Nahdliyah, Milla Alifatun, Tatik Widiharih, and Alan Prahutama. "METODE k-MEDOIDS CLUSTERING DENGAN VALIDASI SILHOUETTE INDEX DAN C-INDEX (Studi Kasus Jumlah Kriminalitas Kabupaten/Kota di Jawa Tengah Tahun 2018)." Jurnal Gaussian 8, no. 2 (2019): 161–70. http://dx.doi.org/10.14710/j.gauss.v8i2.26640.

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The k-medoids method is a non-hierarchical clustering to classify n object into k clusters that have the same characteristics. This clustering algorithm uses the medoid as its cluster center. Medoid is the most centrally located object in a cluster, so it’s robust to outliers. In cluster analysis the objects are grouped by the similarity. To measure the similarity, it can be used distance measures, euclidean distance and cityblock distance. The distance that is used in cluster analysis can affect the clustering results. Then, to determine the quality of the clustering results can be used the i
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Religia, Yoga, Arvita Emarilis Intani, and Andi Saputra. "PENGELOMPOKAN MENGGUNAKAN ALGORITMA K-MEDOID UNTUK EVALUASI PERFORMA SISWA." Pelita Teknologi 15, no. 1 (2020): 49–55. http://dx.doi.org/10.37366/pelitatekno.v15i1.281.

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Pendidikan merupakan salah satu pondasi dalam kemajuan suatu bangsa, semakin baik kualitas pendidikan yang diselenggarakan oleh suatu bangsa, maka akan diikuti dengan semakin baiknya kualitas bangsa tersebut. Saat ini, siswa pada jenjang SMA banyak mendapat sorotan. Pasalnya tingkat kenakalan remaja banyak terjadi pada masa-masa SMA [1]. Hal tersebut memerlukan evaluasi performa siswa untuk meningkatkan prestasi siswa dan menghindarkan siswa dari tindakan negatif. Salah satu data yang dapat digunakan terkait performa siswa adalah menggunakan Student Academics Dataset. Student Academics terdiri
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Lutfi, Muhammad, and Fatma Agus Setyaningsih. "IMPLEMENTASI METODE K-MEDOIDS DAN HEAP SORT DALAM PENGELOMPOKAN SISWA PENERIMA DANA BANTUAN OPERASIONAL SEKOLAH DI SMA N 1 PEMANGKAT." Coding Jurnal Komputer dan Aplikasi 11, no. 3 (2023): 368. http://dx.doi.org/10.26418/coding.v11i03.60304.

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Program Bantuan Operasional Sekolah (BOS) merupakan bantuan pendanaan yang diberikan oleh Kementerian Pendidikan dan Kebudayaan (Kemendikbud) kepada sekolah yang dapat dimanfaatkan untuk berbagai biaya operasional sekolah. Terkait dengan pelaksaan pemberian dana BOS kepada siswa, kadang kala terjadi kesulitan untuk menentukan siswa yang benar-benar layak untuk mendapatkan dana BOS tersebut dikarenakan banyaknya siswa di SMA N 1 Pemangkat. Penelitian ini bertujuan untuk membangun sebuah sistem yang mampu menentukan siswa yang layak menerima beasiswa dana BOS tersebut. Dalam penelitian menggunak
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AS, Wardianti, Muhammad Kasim Aidid, and Muhammad Nusrang. "Pengelompokan Kabupaten/Kota Provinsi Sulawesi Selatan dan Barat Berdasarkan Angka Partisipasi Pendidikan SMA/SMK/MA Menggunakan K-Medoid dan CLARA." VARIANSI: Journal of Statistics and Its application on Teaching and Research 1, no. 3 (2019): 48. http://dx.doi.org/10.35580/variansiunm12899.

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Abstrak Analisis kelompok digunakan untuk mengelompokan objek-objek dengan kesamaan karakteristik yang tinggi dalam satu kelompok sementara objek-objek dengan ketidaksamaan karaketristik berada dalam kelompok yang berbeda. Analisis kelompok terbagi menjadi metode hierarki dan non hierarki. Metode hierarki menggunakan struktur seperti pohon pada keputusan pengelompokan objeknya. Metode tidak berhierarki menemptakan objek - objek ke pusat kelompok terdekat saat banyaknya kelompok sudah ditentukan. Pusat kelompok terbagi menjadi rataan dan median. Kelemahan dari pusat kelompok rataan yaitu tidak
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Al Rivan, Muhammad Ezar, Giovani Prakasa Gandi, and Fendy Novianto Lukman. "Perbandingan Hybrid Genetic K-Means++ dan Hybrid Genetic K-Medoid untuk Klasterisasi Dataset EEG Eyestate." PETIR 14, no. 1 (2020): 103–13. http://dx.doi.org/10.33322/petir.v14i1.953.

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K-Means++ and K-Medoids are data clustering methods. The data cluster speed is determined by the iteration value, the lower the iteration value, the faster the data clustering is done. Data clustering performance can be optimized to get more optimal clustering results. One algorithm that can optimize cluster speed is Genetic Algorithm (GA). The dataset used in the study is a dataset of EEG Eyestate. The optimization results before hybrid GA on K-Means++ are the iteration average values is 11.6 to 5,15, and in K-Medoid are the iteration average values decreased from 5.9 to 5.2. Based on the com
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Hudaib, Amjad, Mohammad Khanafseh, and Ola Surakhi. "An Improved Version of K-medoid Algorithm using CRO." Modern Applied Science 12, no. 2 (2018): 116. http://dx.doi.org/10.5539/mas.v12n2p116.

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Clustering is the process of grouping a set of patterns into different disjoint clusters where each cluster contains the alike patterns. Many algorithms had been proposed before for clustering. K-medoid is a variant of k-mean that use an actual point in the cluster to represent it instead of the mean in the k-mean algorithm to get the outliers and reduce noise in the cluster. In order to enhance performance of k-medoid algorithm and get more accurate clusters, a hybrid algorithm is proposed which use CRO algorithm along with k-medoid. In this method, CRO is used to expand searching for the opt
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Lai, Por-Shen, and Hsin-Chia Fu. "Variance enhanced K-medoid clustering." Expert Systems with Applications 38, no. 1 (2011): 764–75. http://dx.doi.org/10.1016/j.eswa.2010.07.030.

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Dissertations / Theses on the topic "K-Medoid"

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Sedláček, Josef. "Algoritmy pro shlukování textových dat." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-218899.

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The thesis deals with text mining. It describes the theory of text document clustering as well as algorithms used for clustering. This theory serves as a basis for developing an application for clustering text data. The application is developed in Java programming language and contains three methods used for clustering. The user can choose which method will be used for clustering the collection of documents. The implemented methods are K medoids, BiSec K medoids, and SOM (self-organization maps). The application also includes a validation set, which was specially created for the diploma thesis
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Hrstic, Dusan Viktor. "Pattern analysis of the user behaviour in a mobile application using unsupervised machine learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-261424.

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Continuously increasing amount of logged data increases the possibilities of finding new discoveries about the user interaction with the application for which the data is logged. Traces from the data may reveal some specific user behavioural patterns which can discover how to improve the development of the application by showing the ways in which the application is utilized. In this thesis, unsupervised machine learning techniques are used in order to group the users depending on their utilization of SEB Privat Android mobile application. The user interactions in the applications are first ext
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Ayanoglu, Cemal Can. "A Location Routing Problem For The Municipal Solid Waste Management System." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12608230/index.pdf.

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This study deals with a municipal solid waste management system in which the strategic and tactical decisions are addressed simultaneously. In the system, the number and locations of the transfer facilities which serve to the particular solid waste pick-up points and the landfill are determined. Additionally, routing plans are constructed for the vehicles which collect the solid waste from the pick-up points by regarding the load capacity of the vehicles and shift time restrictions. We formulate this reverse logistics system as a location-routing problem with two facility layers. Mathematical
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Kondapalli, Swetha. "An Approach To Cluster And Benchmark Regional Emergency Medical Service Agencies." Wright State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=wright1596491788206805.

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Hezoučký, Ladislav. "Nástroj pro shlukovou analýzu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237169.

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The master' s thesis deals with cluster data analysis. There are explained basic concepts and methods from this domain. Result of the thesis is Cluster analysis tool, in which are implemented methods K-Medoids and DBSCAN. Adjusted results on real data are compared with programs Rapid Miner and SAS Enterprise Miner.
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Rodrigues, Fabiene Silva. "Métodos de agrupamento na análise de dados de expressão gênica." Universidade Federal de São Carlos, 2009. https://repositorio.ufscar.br/handle/ufscar/4537.

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Made available in DSpace on 2016-06-02T20:06:03Z (GMT). No. of bitstreams: 1 2596.pdf: 1631367 bytes, checksum: 90f2d842a935f1dd50bf587a33f6a2cb (MD5) Previous issue date: 2009-02-16<br>The clustering techniques have frequently been used in literature to the analyse data in several fields of application. The main objective of this work is to study such techniques. There is a large number of clustering techniques in literature. In this work we concentrate on Self Organizing Map (SOM), k-means, k-medoids and Expectation- Maximization (EM) algorithms. These algorithms are applied to gene expres
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Jurásek, Petr. "Shlukování proteinových sekvencí na základě podobnosti primární struktury." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236761.

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This master's thesis consider clustering of protein sequences based on primary structure of proteins. Studies the protein sequences from they primary structure. Describes methods for similarities in the amino acid sequences of proteins, cluster analysis and clustering algorithms. This thesis presents concept of distance function based on similarity of protein sequences and implements clustering algorithms ANGES, k-means, k-medoids in Python programming language.
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QianFang and 方茜. "Kernel K Medoids Algorithm with Selected Initial Values." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/4paq85.

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碩士<br>國立成功大學<br>統計學系<br>105<br>This study proposes a clustering algorithm that combine gaussian kernel function with k medoids clustering algorithm. In the meanwhile, we use a variable called Vj (Park and Jun, 2009) to rank objects and select the r middle values as our initial centers. The selection of initial values makes the clustering process more efficient, and the combination of gaussian kernel function makes the clustering outcome more resistant to outliers or noises. To evaluate the proposed algorithm, we analyze some real, synthetic and relational datasets and compar- ing with the resu
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Santo, Francisco José Monteiro do Espírito. "An exploratory analysis of pedestrian accidents patterns in Lisbon." Master's thesis, 2022. http://hdl.handle.net/10362/134444.

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Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence<br>Mobility is one of the pillars of Smart Cities, being one of the most important issues for the development and growth of a city. Nowadays, with the massive flow of populations to large urban centers, mobility and its dangers require special attention. The number of pedestrian accidents has been increasing over the past few years, and it is important to understand what causes and factors contribute to them. The main
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Liao, Chun-Yen, and 廖俊彥. "Predicting Residual Useful Lifetime of Circuit Breakers Using K-Medoids and Holt-Winters Algorithms." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/9242a7.

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碩士<br>國立彰化師範大學<br>機電工程學系所<br>105<br>ABSTRACT The purpose of this essay is to focus on the prediction of the circuit breakers’ residual useful lifetime. Now the circuit breakers’useful lifetime is provided by the reliability test which is set up before leaving the factory. Its test conditions are mostly fixed. Such as temperature; electric pressure; are all set up in fixed numerical numbers. But products in reality are changeable in different environment conditions, so the estimates lifetime will be not coincide with the real lifetime. Therefore, the essay will use the insulation resistance val
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Book chapters on the topic "K-Medoid"

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Zhang, Qiaoping, and Isabelle Couloigner. "A New and Efficient K-Medoid Algorithm for Spatial Clustering." In Computational Science and Its Applications – ICCSA 2005. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11424857_20.

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Chu, Shu-Chuan, John F. Roddick, and J. S. Pan. "An Efficient K-Medoids-Based Algorithm Using Previous Medoid Index, Triangular Inequality Elimination Criteria, and Partial Distance Search." In Data Warehousing and Knowledge Discovery. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46145-0_7.

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Dogra, Priya, and Rakesh Kumar. "Fuzzy K-Medoid Clustering Strategy for Heterogeneous and Dynamic Data for IoT Scenario." In Proceedings of International Conference on IoT Inclusive Life (ICIIL 2019), NITTTR Chandigarh, India. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3020-3_26.

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Agarwal, Sakshi, and Shikha Mehta. "GA with k-Medoid Approach for Optimal Seed Selection to Maximize Social Influence." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1286-5_9.

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Lalwani, Amar, Sriparna Banerjee, Manisha Mouly Kindo, and Syed Zishan Ali. "An Obscure Method for Clustering in Android Using k-Medoid and Apriori Algorithm." In Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 1. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63673-3_9.

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Khan, Huma, and S. M. Ghosh. "Machine Learning Approach for Crop Yield Prediction Emphasis on K-Medoid Clustering and Preprocessing." In International Conference on Intelligent Computing and Smart Communication 2019. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0633-8_27.

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Garikapati, Pruthvi Raju, K. Balamurugan, T. P. Latchoumi, and G. Shankar. "A Quantitative Study of Small Dataset Machining by Agglomerative Hierarchical Cluster and K-Medoid." In Lecture Notes in Electrical Engineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8774-7_59.

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Ningrum, Ariska Fitriyana, Alwan Fadlurohman, Ihsan Fathoni Amri, Saeful Amri, and Dannu Purwanto. "Exploring Patterns of Educational Participation through K-Medoid: A Case Study of Central Java." In Advances in Social Science, Education and Humanities Research. Atlantis Press SARL, 2025. https://doi.org/10.2991/978-2-38476-428-0_5.

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Jaiswal, Ayshwarya, Vijay Kumar Dwivedi, and Om Prakash Yadav. "Analyzing and Enhancing Processing Speed of K-Medoid Algorithm Using Efficient Large Scale Processing Frameworks." In Hybrid Intelligent Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49336-3_14.

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Sharma, Garima, Praveen Kumar, and Laxmi Shrivastava. "An Efficient Performance of Enhanced Bellman-Ford Algorithm in Wireless Sensor Network Using K-Medoid Clustering." In Intelligent Computing Applications for Sustainable Real-World Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44758-8_6.

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Conference papers on the topic "K-Medoid"

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Morris, Jordan, and Alex Yakovlev. "Pre-Sorted Tsetlin Machine (the Genetic K-Medoid Method)." In 2024 International Symposium on the Tsetlin Machine (ISTM). IEEE, 2024. https://doi.org/10.1109/istm62799.2024.10931611.

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Thiyagarajan, N., and N. Shanmugasundaram. "Accessing the Performance of K-Medoid, K-Means and FCM Clustering Techniques for Wireless Sensor Networks." In 2024 IEEE 5th India Council International Subsections Conference (INDISCON). IEEE, 2024. http://dx.doi.org/10.1109/indiscon62179.2024.10744273.

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Shayamalagowri, Chandran, and Sivaswamy Susila Sakthy. "Artificial Rabbit Combined K-medoid Golden Eagle Optimization for Energy Efficient Routing in WSN." In 2025 3rd International Conference on Advancement in Computation & Computer Technologies (InCACCT). IEEE, 2025. https://doi.org/10.1109/incacct65424.2025.11011321.

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Li, Ziyang, Zhenlan Dou, Xun Dou, et al. "Improved K-Medoids Clustering Algorithm Based on Load Feature Set." In 2024 5th International Conference on Clean Energy and Electric Power Engineering (ICCEPE). IEEE, 2024. https://doi.org/10.1109/iccepe62686.2024.10931648.

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Harikumar, Sandhya, and Shabana Shamsuddin Thaha. "MapReduce model for k-medoid clustering." In 2016 International Conference on Data Science and Engineering (ICDSE). IEEE, 2016. http://dx.doi.org/10.1109/icdse.2016.7823940.

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Hadi, Youssef, Fedwa Essannouni, and Rachid Oulad Haj Thami. "Video summarization by k-medoid clustering." In the 2006 ACM symposium. ACM Press, 2006. http://dx.doi.org/10.1145/1141277.1141601.

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Aljoby, W., and K. Alenezi. "Parallelization of K-medoid clustering algorithm." In 2013 5th International Conference on Information and Communication Technology for the Muslim World 2013 (ICT4M). IEEE, 2013. http://dx.doi.org/10.1109/ict4m.2013.6518923.

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Tamura, Yusuke, and Sadaaki Miyamoto. "Two-stage clustering using one-pass K-medoids and medoid-based agglomerative hierarchical algorithms." In 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS). IEEE, 2014. http://dx.doi.org/10.1109/scis-isis.2014.7044641.

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Halder, Amiya, Avijit Dasgupta, and Susmita Ghosh. "Image segmentation using rough-fuzzy K-medoid algorithm." In 2012 International Conference on Communications, Devices and Intelligent Systems (CODIS). IEEE, 2012. http://dx.doi.org/10.1109/codis.2012.6422147.

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Sreenivasan, Sreeram C., and Srikrishna Bhashyam. "Sequential Nonparametric K-Medoid Clustering of Data Streams." In 2022 National Conference on Communications (NCC). IEEE, 2022. http://dx.doi.org/10.1109/ncc55593.2022.9806794.

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