Relevant bibliographies by topics / K-medoids algorithm and classifier

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

Academic literature on the topic 'K-medoids algorithm and classifier'

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

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Journal articles on the topic "K-medoids algorithm and classifier"

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K.Yohanu and M.Pradeep. "A ROBUST FACE RECOGNITION APPROACH THROUGH DIFFERENT LOCAL FEATURES." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 4, no. 9 (2017): 14–19. https://doi.org/10.5281/zenodo.888845.

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In this paper, we proposed method is a robust face recognition approach through different local features has been implemented. We used YALE face database in this paper. First the images are partitioned into different sub bands using discrete wavelet transform. Kernel methods are widely used for to extract the features from the different sub bands for non linear methods. sub band methods are used for extract the local features from the all sub bands. Then the all local features are formed as global features to improve the recognition efficiency. As compared to linear methods most of the researc
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Vaishali, M.Kumbhakarna, B. Kulkarni Sonali, and D.Dhawale Apurva. "NLP Algorithms Endowed for Automatic Extraction of Information from Unstructured Free-Text Reports of Radiology Monarchy." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 9, no. 12 (2020): 338–43. https://doi.org/10.35940/ijitee.L8009.1091220.

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Natural Language Processing (NLP) Algorithms are the key factors for automatic information extraction form the unstructured free-text radiology reports .To extract clinically important findings and recommendations, various NLP algorithms are used . A rule-based NLP system is used in most of the automated IE applications in medical domain; whereas some applications are using Random Forest classifier, PageRank Algorithm, clustering algorithm, Conditional Random Fields (CRF) algorithm, and deep learning-based approaches. Some papers found with methods used for IE like, Support Vector Machines (SV
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Ahmed, Asraa Safaa, Zainab Kadhm Obeas, Batool Abd Alhade, and Refed Adnan Jaleel. "Improving prediction of plant disease using k-efficient clustering and classification algorithms." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 3 (2022): 939. http://dx.doi.org/10.11591/ijai.v11.i3.pp939-948.

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<span lang="EN-US">Because plant disease is main cause of most plants’ damage, improving prediction plans for early detection of plant where it has disease or not is an essential interest of decision makers in the agricultural sector for providing proper plant care at appropriate time. Clustering and classification algorithms have proven effective in early detection of plant disease. Making clusters of plants with similar features is an excellent strategy for analyzing features and providing an overview of care quality provided to similar plants. Thus, in this article, we present an arti
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RamachandraRao, Kurada, K. Karteeka Pavan Dr., Rajeswari M, and Lakshmi Kamala M. "NOVEL TEXT CATEGORIZATION BY AMALGAMATION OF AUGMENTED K-NEAREST NEIGHBOURHOODCLASSIFICATION AND K-MEDOIDS CLUSTERING." International Journal of Computational Science and Information Technology (IJCSITY) 1, November (2020): 1–13. https://doi.org/10.5281/zenodo.3716106.

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<strong>Abstract </strong> Machine learning for text classification is the underpinning of document cataloging, news filtering, document steering and exemplification. In text mining realm, effective feature selection is significant to make the learning task more accurate and competent. One of the traditional lazy text classifier k-Nearest Neighborhood (kNN) has a major pitfall in calculating the similarity between all the objects in training and testing sets, there by leads to exaggeration of both computational complexity of the algorithm and massive consumption of main memory. To diminish the
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Asraa, Safaa Ahmed, Kadhm Obeas Zainab, Abd Alhade Batool, and Adnan Jaleel Refed. "Improving prediction of plant disease using k-efficient clustering and classification algorithms." International Journal of Artificial Intelligence (IJ-AI) 11, no. 3 (2022): 939–48. https://doi.org/10.11591/ijai.v11.i3.pp939-948.

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Because plant disease is main cause of most plants&rsquo; damage, improving prediction plans for early detection of plant where it has disease or not is an essential interest of decision makers in the agricultural sector for providing proper plant care at appropriate time. Clustering and classification algorithms have proven effective in early detection of plant disease. Making clusters of plants with similar features is an excellent strategy for analyzing features and providing an overview of care quality provided to similar plants. Thus, in this article, we present an artificial intelligence
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Abdillah, Muhammad Oemar, and Raissa Amanda Putri. "Stock Grouping Based on Price Earnings Ratio and Price Book Value Using K-Medoids Algorithm." Journal of Information Systems and Informatics 6, no. 3 (2024): 1704–22. http://dx.doi.org/10.51519/journalisi.v6i3.809.

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Investing involves allocating funds to achieve optimal returns by evaluating opportunities and managing risks in asset acquisition. Recently, many news reports have highlighted issues in the Indonesian capital market, such as stock investors using online loan funds for trading, which often leads to debt. This research aims to apply the K-Medoids algorithm for stock clustering, enabling investors to select fundamentally sound stocks based on the Price-Earnings Ratio (PER) and Price-Book Value (PBV). The K-Medoids method results show that Cluster 1 includes 93 stocks with moderate PER and PBV va
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Wang, Yonghua, Shunchao Zhang, Yongwei Zhang, Pin Wan, Jiangfan Li, and Nan Li. "A Cooperative Spectrum Sensing Method Based on Empirical Mode Decomposition and Information Geometry in Complex Electromagnetic Environment." Complexity 2019 (February 18, 2019): 1–13. http://dx.doi.org/10.1155/2019/5470974.

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In a complex electromagnetic environment, there are cases where the noise is uncertain and difficult to estimate, which poses a great challenge to spectrum sensing systems. This paper proposes a cooperative spectrum sensing method based on empirical mode decomposition and information geometry. The method mainly includes two modules, a signal feature extraction module and a spectrum sensing module based on K-medoids. In the signal feature extraction module, firstly, the empirical modal decomposition algorithm is used to denoise the signals collected by the secondary users, so as to reduce the i
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Mulyawan, Alif Rizqi, Deni Gunawan, Hasan Basri, Salman Alfarizi, and Nurul Ichsan. "Penerapan K-Medoids Clustering Dan Silhouette Method Untuk Strategi Pemasaran Program Donasi Pada Lembaga Amil Zakat." INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System 8, no. 1 (2023): 107. http://dx.doi.org/10.51211/isbi.v8i1.2468.

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Donation data management is a complex challenge for amil zakat institutions in designing an effective marketing strategy for fundraising programs. In this study, the k-medoids algorithm was used to cluster the donation data with the aim of identifying patterns and characteristics of donors. The k-medoids algorithm was chosen because of its ability to handle unusual data and non-numeric attributes. Through clustering analysis, this study classifies donors based on attributes such as the number of donations, the frequency of donations, and the time interval for donating. And in determining the n
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Tan, Shin Yee, Muhammad Firdaus Akbar, Nawaf H. M. M. Shrifan, Ghassan Nihad Jawad, and Mohd Nadhir Ab Wahab. "Assessment of Defects under Insulation Using K-Medoids Clustering Algorithm-Based Microwave Nondestructive Testing." Coatings 12, no. 10 (2022): 1440. http://dx.doi.org/10.3390/coatings12101440.

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Composite insulations, such as ceramics, are commonly utilized in the turbine system as a thermal coating barrier to protect the metal substrate against high temperatures and pressure. The presence of delamination in the composite insulations may cause turbine failure, leading to a catastrophic accident. Thus, regular non-destructive testing is required to detect and evaluate insulation defects. Among the non-destructive testing techniques, the microwave technique has emerged as a promising method for assessing defects in ceramic coatings. Although the method is promising, microwave non-destru
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Supriyatna, Samso. "Klasifikasi Data Menggunakan Algoritma K-Means Clustering Dan Naive Bayes Classifier Berdasarkan Analisa Tekstur Metode Gray Level Co-Occurrence Matrix (GLCM)." SAINSTECH: JURNAL PENELITIAN DAN PENGKAJIAN SAINS DAN TEKNOLOGI 33, no. 1 (2023): 5–14. http://dx.doi.org/10.37277/stch.v33i1.1647.

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ABSTRACT&#x0D; &#x0D; Data classification in the system is widely used in various fields of information technology, data classification is obtained by analyzing objects based on the level of similarity and mapping the characteristics that are protected in the object based on the specified cluster. The use of classification in this study by use texture patterns contained in data by use K-Means Clustering algorithm and Naive Bayes Classifier. The K-Means Clustering algorithm is the process of grouping data based on the same characteristics, while Naive Bayes Classifier use probability and statis
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Dissertations / Theses on the topic "K-medoids algorithm and classifier"

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Mestre, Ricardo Jorge Palheira. "Improvements on the KNN classifier." Master's thesis, Faculdade de Ciências e Tecnologia, 2013. http://hdl.handle.net/10362/10923.

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Dissertação para obtenção do Grau de Mestre em Engenharia Informática<br>The object classification is an important area within the artificial intelligence and its application extends to various areas, whether or not in the branch of science. Among the other classifiers, the K-nearest neighbor (KNN) is among the most simple and accurate especially in environments where the data distribution is unknown or apparently not parameterizable. This algorithm assigns the classifying element the major class in the K nearest neighbors. According to the original algorithm, this classification implies the
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Gupta, Nidhi. "Mutual k Nearest Neighbor based Classifier." University of Cincinnati / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1289937369.

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Neo, TohKoon. "A Direct Algorithm for the K-Nearest-Neighbor Classifier via Local Warping of the Distance Metric." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2168.pdf.

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Neo, Toh Koon Charlie. "A direct boosting algorithm for the k-nearest neighbor classifier via local warping of the distance metric /." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2168.pdf.

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Sahin, Ferat. "A Radial Basis Function Approach to a Color Image Classification Problem in a Real Time Industrial Application." Thesis, Virginia Tech, 1997. http://hdl.handle.net/10919/36847.

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In this thesis, we introduce a radial basis function network approach to solve a color image classification problem in a real time industrial application. Radial basis function networks are employed to classify the images of finished wooden parts in terms of their color and species. Other classification methods are also examined in this work. The minimum distance classifiers are presented since they have been employed by the previous research. We give brief definitions about color space, color texture, color quantization, color classification methods. We also give an intensive review of
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Duan, Haoyang. "Applying Supervised Learning Algorithms and a New Feature Selection Method to Predict Coronary Artery Disease." Thèse, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31113.

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From a fresh data science perspective, this thesis discusses the prediction of coronary artery disease based on Single-Nucleotide Polymorphisms (SNPs) from the Ontario Heart Genomics Study (OHGS). First, the thesis explains the k-Nearest Neighbour (k-NN) and Random Forest learning algorithms, and includes a complete proof that k-NN is universally consistent in finite dimensional normed vector spaces. Second, the thesis introduces two dimensionality reduction techniques: Random Projections and a new method termed Mass Transportation Distance (MTD) Feature Selection. Then, this thesis compares t
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Guňka, Jiří. "Adaptivní klient pro sociální síť Twitter." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-237052.

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The goal of this term project is create user friendly client of Twitter. They may use methods of machine learning as naive bayes classifier to mentions new interests tweets. For visualissation this tweets will be use hyperbolic trees and some others methods.
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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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Dočekal, Martin. "Porovnání klasifikačních metod." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-403211.

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This thesis deals with a comparison of classification methods. At first, these classification methods based on machine learning are described, then a classifier comparison system is designed and implemented. This thesis also describes some classification tasks and datasets on which the designed system will be tested. The evaluation of classification tasks is done according to standard metrics. In this thesis is presented design and implementation of a classifier that is based on the principle of evolutionary algorithms.
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Jiao, Lianmeng. "Classification of uncertain data in the framework of belief functions : nearest-neighbor-based and rule-based approaches." Thesis, Compiègne, 2015. http://www.theses.fr/2015COMP2222/document.

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Dans de nombreux problèmes de classification, les données sont intrinsèquement incertaines. Les données d’apprentissage disponibles peuvent être imprécises, incomplètes, ou même peu fiables. En outre, des connaissances spécialisées partielles qui caractérisent le problème de classification peuvent également être disponibles. Ces différents types d’incertitude posent de grands défis pour la conception de classifieurs. La théorie des fonctions de croyance fournit un cadre rigoureux et élégant pour la représentation et la combinaison d’une grande variété d’informations incertaines. Dans cette thè
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Book chapters on the topic "K-medoids algorithm and classifier"

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Şenel, Fatih Ahmet, Asım Sinan Yüksel, and Tuncay Yiğit. "Feature Selection by Using DE Algorithm and k-NN Classifier." In Artificial Intelligence and Applied Mathematics in Engineering Problems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36178-5_77.

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Hu, Zhenbang, and Zhihua Cai. "Feature Synthesis Algorithm Combined with k-NN Classifier for Spectral Data Classification." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16388-3_28.

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Therese, M. Julie, A. Devi, and G. Kavya. "Melanoma Detection on Skin Lesion Images Using K-Means Algorithm and Svm Classifier." In Handbook of Deep Learning in Biomedical Engineering and Health Informatics. Apple Academic Press, 2021. http://dx.doi.org/10.1201/9781003144694-9.

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Lu, Bolin. "Research and Application of Digit Recognition Based on K-Nearest Neighbor Classifier." In Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023). Atlantis Press International BV, 2024. http://dx.doi.org/10.2991/978-94-6463-370-2_5.

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Mohana Priya, R., L. K. Hema, V. Vanitha, and R. Karthikeyan. "Classification and Detection of Malarial Parasite in Blood Samples Using K-Means Clustering Algorithm and Support Vector Machine Classifier." In Micro-Electronics and Telecommunication Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4687-1_39.

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Liu, Yang, Soon-Thiam Khu, and Dragon Savic. "A Hybrid Optimization Method of Multi-objective Genetic Algorithm (MOGA) and K-Nearest Neighbor (KNN) Classifier for Hydrological Model Calibration." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28651-6_80.

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Pérez-Ortega, Joaquín, Nelva N. Almanza-Ortega, Jessica Adams-López, et al. "Improving the Efficiency of the K-medoids Clustering Algorithm by Getting Initial Medoids." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56535-4_13.

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Drias, Habiba, Nadjib Fodil Cherif, and Amine Kechid. "k-MM: A Hybrid Clustering Algorithm Based on k-Means and k-Medoids." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27400-3_4.

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Yee, Tan Shin, Muhammad Firdaus Akbar, Nor Azlin Ghazali, Ghassan Nihad Jawad, and Nawaf H. M. M. Shrifan. "Microwave Non-destructive Testing Using K-Medoids Clustering Algorithm." In Proceedings of the 12th International Conference on Robotics, Vision, Signal Processing and Power Applications. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9005-4_42.

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Wang, Xiaochun, Xiali Wang, and Don Mitchell Wilkes. "An Efficient K-Medoids Clustering Algorithm for Large Scale Data." In Machine Learning-based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9217-7_5.

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Conference papers on the topic "K-medoids algorithm and classifier"

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Alexander, Ivan, Yaya Heryadi, Lukas, Wayan Suparta, and Ilvico Sonata. "Classifying Tweet Sentiment Analysis on Airlines using Random Forest Classifier, Naïve Bayes Classifier and K-Nearest Neighbors Classifier Algorithm." In 2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP). IEEE, 2023. https://doi.org/10.1109/airosip58759.2023.10873986.

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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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Ruchitha, G. Sai, V. Karthick, and Iffat Nasim. "Expression of Concern for: A Novel Approach to Find Accuracy in Credit Card Fraud Detection Using Improved K-Nearest Neighbor Classifier Method Comparing with Logistic Regression Algorithm." In 2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS). IEEE, 2022. http://dx.doi.org/10.1109/macs56771.2022.10703548.

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Zhang, Pan, Zhaoyu Chen, and Yang Yue. "A distribution automation quantum key construction algorithm based on K-medoids." In Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024), edited by Pierluigi Siano and Wenbing Zhao. SPIE, 2024. http://dx.doi.org/10.1117/12.3034454.

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Esti, Nimas Maharani Cahyaning, Mela Rosiana Dewi, Najwa Khoirun Nada Anry, Gregorius Halilintar Zelot Pratikno, and Aripin. "Implementation K-Nearest Neighbor Algorithm Skin Diseases Classifier." In 2023 International Seminar on Application for Technology of Information and Communication (iSemantic). IEEE, 2023. http://dx.doi.org/10.1109/isemantic59612.2023.10295304.

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Yunlong Gao, Jin-yan Pan, and Feng Gao. "Improved boosting algorithm through weighted k-nearest neighbors classifier." In 2010 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccsit.2010.5563551.

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Pacaldo, Joren Mundane, Chi Wee Tan, Wah Pheng Lee, Dustin Gerard Ancog, and Haroun Al Raschid Christopher Macalisang. "Utilizing Synthetically-Generated License Plate Automatic Detection and Recognition of Motor Vehicle Plates in Philippines." In International Conference on Digital Transformation and Applications (ICDXA 2021). Tunku Abdul Rahman University College, 2021. http://dx.doi.org/10.56453/icdxa.2021.1022.

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We investigated the potential use of synthetic data for automatic license plate detection and recognition by detecting and clustering each of the characters on the license plates. We used 36 cascading classifiers (26 letters + 10 numbers) as an individual character to detect synthetically generated license plates. We trained our cascade classifier using a Local Binary Pattern (LBP) as the visual descriptor. After detecting all the characters individually, an investigation has been established in identifying and utilizing a clustering algorithm in grouping these characters for valid license pla
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Venkataravana Nayak, K., J. Arunalatha, and K. Venugopal. "IR-FF-kNN: Image Retrieval Using Feature Fusion with k-Nearest Neighbour Classifier." In WABD 2021: 2021 Workshop on Algorithm and Big Data. ACM, 2021. http://dx.doi.org/10.1145/3456389.3456405.

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Li, Heng, and Long Yang. "Analysis of Local-LDtree classifier based on K-nearest Neighbor algorithm." In 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet). IEEE, 2012. http://dx.doi.org/10.1109/cecnet.2012.6201792.

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Maximov, Yury, Massih-Reza Amini, and Zaid Harchaoui. "Rademacher Complexity Bounds for a Penalized Multi-class Semi-supervised Algorithm (Extended Abstract)." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/800.

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We propose Rademacher complexity bounds for multi-class classifiers trained with a two-step semi-supervised model. In the first step, the algorithm partitions the partially labeled data and then identifies dense clusters containing k predominant classes using the labeled training examples such that the proportion of their non-predominant classes is below a fixed threshold stands for clustering consistency. In the second step, a classifier is trained by minimizing a margin empirical loss over the labeled training set and a penalization term measuring the disability of the learner to predict the
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Reports on the topic "K-medoids algorithm and classifier"

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Searcy, Stephen W., and Kalman Peleg. Adaptive Sorting of Fresh Produce. United States Department of Agriculture, 1993. http://dx.doi.org/10.32747/1993.7568747.bard.

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This project includes two main parts: Development of a “Selective Wavelength Imaging Sensor” and an “Adaptive Classifiery System” for adaptive imaging and sorting of agricultural products respectively. Three different technologies were investigated for building a selectable wavelength imaging sensor: diffraction gratings, tunable filters and linear variable filters. Each technology was analyzed and evaluated as the basis for implementing the adaptive sensor. Acousto optic tunable filters were found to be most suitable for the selective wavelength imaging sensor. Consequently, a selectable wave
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Multiple Engine Faults Detection Using Variational Mode Decomposition and GA-K-means. SAE International, 2022. http://dx.doi.org/10.4271/2022-01-0616.

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As a critical power source, the diesel engine is widely used in various situations. Diesel engine failure may lead to serious property losses and even accidents. Fault detection can improve the safety of diesel engines and reduce economic loss. Surface vibration signal is often used in non-disassembly fault diagnosis because of its convenient measurement and stability. This paper proposed a novel method for engine fault detection based on vibration signals using variational mode decomposition (VMD), K-means, and genetic algorithm. The mode number of VMD dramatically affects the accuracy of ext
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