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

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

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’ 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  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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Bedre, Jyoti S., and P. Lakshmi Prasanna. "Novel SR-RNN Classifier for Accurate Emotion Detection in Facial Analysis." Statistics, Optimization & Information Computing 13, no. 4 (2024): 1557–77. https://doi.org/10.19139/soic-2310-5070-2142.

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Facial Expression Recognition (FER) is crucial for understanding human emotions in fields like human-computer interaction and psychology. Despite advances in deep learning (DL), existing FER methods often struggle with noise, lighting variations, and inter-subject variability, leading to inaccurate emotion classification. This paper addresses these challenges by proposing a novel SwikyRelu Recurrent Neural Network (SR-RNN) classifier. The aim is to enhance FER accuracy while reducing computational complexity. The methodology involves a multi-step process starting with image pre-processing usin

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Arisawati, Ester, Rinawati Rinawati, Erene Gernaria Sihombing, Frisma Handayanna, and Linda Sari Dewi. "WEBSITE DESIGN FOR NUTRITION STATUS CLASSIFICATION OF TODDLERS USING UI/X WITH K-MEDOIDS ALGORITHM." Jurnal Komputer dan Informatika 13, no. 1 (2025): 51–59. https://doi.org/10.35508/jicon.v13i1.21164.

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Nutritional needs in Indonesia vary based on age, gender, physical activity, and an individual's health condition. According to Regulation of the Minister of Health of the Republic of Indonesia No. 28 of 2019, the Recommended Dietary Allowance (RDA) issued by the Ministry of Health provides guidelines on daily energy (calorie) requirements. For infants aged 0 to 12 months, the required intake is 550–725 kcal. The toddler phase (0–5 years old) is a golden period of growth, during which physical and brain development occurs rapidly. Malnutrition during this period can lead to growth disorders su

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Yang, Ling Zhi, Rong Zhu, Kai Dong, Wen Juan Liu, and Guo Hong Ma. "Study of Optimizing Combined-Blowing in EAF Based on K-Medoids Clustering Algorithm." Advanced Materials Research 881-883 (January 2014): 1540–44. http://dx.doi.org/10.4028/www.scientific.net/amr.881-883.1540.

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The integrated technology of oxygen blowing and bottom blowing was the core of EAF combined-blowing. Today's process model can not exactly describe the control of combined-blowing. In this paper, the EAF steelmaking history data was analyzed by K-medoids clustering algorithm which is data mining technology. Different clustering results were obtained under the cost of steelmaking, oxygen consumption, and etc. Finally, the optimizing combined-blowing curve is found out by analyzing the classified results. In the early of smelting, the flow rate of No.1 oxygen lance and No.2 oxygen lance is incre

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Du, Hongle, Yan Zhang, Lin Zhang, and Yeh-Cheng Chen. "Selective ensemble learning algorithm for imbalanced dataset." Computer Science and Information Systems, no. 00 (2023): 23. http://dx.doi.org/10.2298/csis220817023d.

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Under the imbalanced dataset, the performance of the base-classifier, the computing method of weight of base-classifier and the selection method of the base-classifier have a great impact on the performance of the ensemble classifier. In order to solve above problem to improve the generalization performance of ensemble classifier, a selective ensemble learning algorithm based on under-sampling for imbalanced dataset is proposed. First, the proposed algorithm calculates the number K of under-sampling samples according to the relationship between class sample density. Then, we use the improved K

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Doi, Maria Dafrosa, Agus Rusgiyono, and Triastuti Wuryandari. "ANALISIS k-MEDOIDS DENGAN VALIDASI INDEKS PADA IPM DAERAH 3T DI INDONESIA." Jurnal Gaussian 12, no. 2 (2023): 178–88. http://dx.doi.org/10.14710/j.gauss.12.2.178-188.

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Human development is a development paradigm that places humans as the main target of all development activities, namely controling over resources, improving health and improving education. The Human Development Index (HDI) in Indonesia varies in each district, especially in the 3T areas. The 3T area is an area that is classified as underdeveloped, remote and outermost in terms of economy, health, education and infrastructure. The k-Medoids method is a partitional clustering method for grouping several objects into clusters. This clustering algorithm uses the medoid as the center of the cluster

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Park, Chong-Sun, and Kyun Huh. "Optimal k-Nearest Neighborhood Classifier Using Genetic Algorithm." Communications for Statistical Applications and Methods 17, no. 1 (2010): 17–27. http://dx.doi.org/10.5351/ckss.2010.17.1.017.

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Meng, Qing. "Credit Card Fraud Detection Using Feature Fusion-based Machine Learning Model." Highlights in Science, Engineering and Technology 23 (December 3, 2022): 111–16. http://dx.doi.org/10.54097/hset.v23i.3208.

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Credit card fraud is a very common means of financial fraud at present. In order to reduce social and personal economic property losses, this study aims to propose a model to predict credit card fraud, so as to detect possible fraudulent transactions in the transaction process. In this paper, the data set to simulate fraudulent transactions be used, and construct the model after processing the characteristic data and selecting the features. In the model construction part, four algorithms are used as the trained classifiers, which are K Nearest Neighbor algorithm, Bgging algorithm, Logistic Reg

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Alfia Nurlaili Tahiyat, Bima Maulana, Ade Eka Saputra, Lusiana Efrizoni, and Rahmaddeni Rahmaddeni. "Klasterisasi Lagu Populer dan Eksplorasi Subgenre Spotify 2024 dengan K-Medoids." Jurnal Informatika Dan Tekonologi Komputer (JITEK) 5, no. 1 (2025): 34–48. https://doi.org/10.55606/jitek.v5i1.5699.

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Spotify's genre classification system remains too broad, often grouping songs with distinct characteristics into the same category. For example, Pop Ballads and Dance Pop are frequently classified under "Pop" despite significant differences in tempo, emotion, and production style. This leads to inaccurate song recommendations. This study applies the K-Medoids algorithm to enhance song classification based on Spotify Playlist Count, Spotify Playlist Reach, and Spotify Popularity. The CRISP- DM methodology guides business understanding, data understanding, data preparation, modeling, evaluation,

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Hapsoro, Agung Nugroho, Subyantara Wicaksana Haryas, Hariyanto, and H. Virgianto Rista. "Algorithm performance comparison for earthquake signal recognition on smartphone's accelerometer." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 5 (2020): 2505~2516. https://doi.org/10.12928/TELKOMNIKA.v18i5.14708.

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Micro-electro-mechanical-system accelerometer is able to detect acceleration signal caused by earthquake. Such type of accelerometer is also used by smartphones. There are few algorithms that can be used to recognize the type of acceleration signal from smartphone. This study aims to find signal recognition algorithm in order to consider the most proper algorithm for earthquake signal detection. The initial stage of designing the recognizer is data collection for each type of signal classification. The next step is to apply a highpass filter to separate the signals collected from the gravitati

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Ha, Hsin-Yu, Fausto C. Fleites, and Shu-Ching Chen. "Content-Based Multimedia Retrieval Using Feature Correlation Clustering and Fusion." International Journal of Multimedia Data Engineering and Management 4, no. 2 (2013): 46–64. http://dx.doi.org/10.4018/jmdem.2013040103.

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Nowadays, only processing visual features is not enough for multimedia semantic retrieval due to the complexity of multimedia data, which usually involve a variety of modalities, e.g. graphics, text, speech, video, etc. It becomes crucial to fully utilize the correlation between each feature and the target concept, the feature correlation within modalities, and the feature correlation across modalities. In this paper, the authors propose a Feature Correlation Clustering-based Multi-Modality Fusion Framework (FCC-MMF) for multimedia semantic retrieval. Features from different modalities are com

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Jagath Prasad, Himayavardhini, and Roji Marjorie S. "Optimized k-nearest neighbours classifier based prediction of epileptic seizures." Bulletin of Electrical Engineering and Informatics 13, no. 4 (2024): 2442–55. http://dx.doi.org/10.11591/eei.v13i4.6598.

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Epileptic seizure is an unstable condition of the brain that cause severe mental disorder and can be fatal if not properly diagnosed at an early stage. Electroencephalogram (EEG) plays a major role in early diagnosis of epileptic seizures. The volume of medical databases is enormous. Classification may become less accurate if the dataset contains redundant and irrelevant attributes. To reduce the mortality rate due to epilepsy, a decision support system that can assist medical professionals in taking immediate precautionary measures prior to reaching the critical condition is required. In this

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Deep, Vasu, and Himanshu Sharma. "SVM Classifier on K-means Clustering Algorithm with Normalization in Data Mining for Prediction." International Journal on Recent and Innovation Trends in Computing and Communication 7, no. 6 (2019): 29–34. http://dx.doi.org/10.17762/ijritcc.v7i6.5318.

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This work is belonging to K-means clustering algorithms classifier is used with this algorithm to classified data and Min Max normalization technique also used is to enhance the results of this work over simply K- Means algorithm. K-means algorithm is a clustering algorithm and basically used for discovering the cluster within a dataset. Here cancer dataset is used for this research work and dataset is classified in two categories – Cancer and Non-Cancer, after execution of the implemented algorithm with SVM and Normalization technique. The initial point selection effects on the results of the

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Sokolov, Anton, Egor Dmitriev, Cyril Gengembre, and Hervé Delbarre. "Automated Classification of Regional Meteorological Events in a Coastal Area Using In Situ Measurements." Journal of Atmospheric and Oceanic Technology 37, no. 4 (2020): 723–39. http://dx.doi.org/10.1175/jtech-d-19-0120.1.

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AbstractThe problem is considered of atmospheric meteorological events’ classification, such as sea breezes, fogs, and high winds, in coastal areas. In situ wind, temperature, humidity, pressure, radiance, and turbulence meteorological measurements are used as predictors. Local atmospheric events of 2013–14 were analyzed and classified manually using data of the measurement campaign in the coastal area of the English Channel in Dunkirk, France. The results of that categorization allowed the training of a few supervised classification algorithms using the data of an ultrasonic anemometer as pre

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R., Gayathri, Ragavi S., Dharani J., and Aasha M. "Sketch Photosynthesis and Classification using KNearest Neighbor Classifier." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 5 (2020): 18–20. https://doi.org/10.35940/ijeat.D9140.069520.

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Sketch-photosynthesis approach is sketching the face of the photograph. Sketch-photosynthesis makes use of a strategy of equivalent composite comic strip with photograph. The capabilities are separated and combined to teach the system. In this paper caricature photosynthesis using K-means and Knearest neighbor classifier is used to sketch the photograph. The K-means algorithm in data mining starts from the primary organization of randomly decided on centroids. It is practiced as the starting point for every cluster because it plays iterative calculations to optimize the positions of the centro

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Papaioannou, Nikolaos, Alkiviadis Tsimpiris, Christos Talagozis, et al. "Parallel Feature Subset Selection Wrappers Using k-means Classifier." WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS 20 (March 9, 2023): 76–86. http://dx.doi.org/10.37394/23209.2023.20.10.

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In a world where the volume of data is constantly increasing, the implementation time of various processes increases significantly. Therefore, the proper management and the effort to reduce the dimensions of the datasets are considered imperative. Feature selection can reduce the size of the datasets by keeping a smaller subset, while improving the accuracy of the classification. The main purpose of this paper is to propose and examine the efficiency of parallel feature selection wrappers based on k-means classifier. The simple kmeans algorithm and a parallel version of it are used. Different

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B.N., Beena Ullala Mata, and Meenakshi Dr.M. "Mammogram Image Segmentation by Watershed Algorithm and Classification through k-NN Classifier." Bonfring International Journal of Advances in Image Processing 8, no. 1 (2018): 01–07. http://dx.doi.org/10.9756/bijaip.8352.

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Mata, B. N. Beena Ullala, and Meenakshi Dr.M. "Mammogram Image Segmentation by Watershed Algorithm and Classification through k-NN Classifier." Bonfring International Journal of Industrial Engineering and Management Science 8, no. 1 (2018): 01–05. http://dx.doi.org/10.9756/bijiems.8352.

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Muhammad Tibri Syofyan, Nonong Amalita, Dodi Vionanda, and Dina Fitria. "Comparison of Distance Function in K-Nearest Neighbor Algorithm to Predict Prospective Customers in Term Deposit Subscriptions." UNP Journal of Statistics and Data Science 1, no. 3 (2023): 105–11. http://dx.doi.org/10.24036/ujsds/vol1-iss3/47.

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Data mining is often used to analysis of the big data to obtain new useful information that will be used in the future. One of the best algorithms in data mining is K-Nearest Neighbor (KKN). K-NN classifier is a distance-based classification algorithm. The distance function is a core component in measuring the distance or similarity between the tested data and the training data. Various measure of distance function exist make this a topic of kind literature problems to determining the best distance function for the performance of the K-NN classifier. This study aims to compare which distance f

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Safri, Yofi Firdan, Riza Arifudin, and Much Aziz Muslim. "K-Nearest Neighbor and Naive Bayes Classifier Algorithm in Determining The Classification of Healthy Card Indonesia Giving to The Poor." Scientific Journal of Informatics 5, no. 1 (2018): 18. http://dx.doi.org/10.15294/sji.v5i1.12057.

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Health is a human right and one of the elements of welfare that must be realized in the form of giving various health efforts to all the people of Indonesia. Poverty in Indonesia has become a national problem and even the government seeks efforts to alleviate poverty. For example, poor families have relatively low levels of livelihood and health. One of the new policies of the Sakti Government Card Program issued by the government includes three cards, namely Indonesia Smart Card (KIP), Healthy Indonesia Card (KIS) and Prosperous Family Card (KKS). In this study to determine the feasibility of

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Sawant, Nisha, and Dnyandev Ravindra Khadapkar. "Comparison of the performance of GaussianNB Algorithm, the K Neighbors Classifier Algorithm, the Logistic Regression Algorithm, the Linear Discriminant Analysis Algorithm, and the Decision Tree Classifier Algorithm on same dataset." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (2022): 1654–65. http://dx.doi.org/10.22214/ijraset.2022.48311.

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Abstract: Most educational institutions worldwide have been closed since March 2020 in an effort to slow the spread of the Covid-19 epidemic. More than 90% of students around the world have been influenced by this. In this study, we'll make a prediction about whether or not the Covid-19 epidemic has benefited student performance. Our data will be divided into training and testing datasets, with 80% of the data utilised for training and 20% for testing. To calculate the accuracy of our predictions, we'll use six different algorithms, including the RandomForestClassifier Algorithm, the GaussianN

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Ke, Chenghu, Yuting Shu, and Xizheng Ke. "Research on Indoor Visible Light Location Based on Fusion Clustering Algorithm." Photonics 10, no. 7 (2023): 853. http://dx.doi.org/10.3390/photonics10070853.

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Aiming at the problem of large positioning errors in the boundary area, a new location fingerprint location method based on a fusion clustering algorithm is proposed. This clustering-based method embodies the idea of rough location first and then fine location. Firstly, the edge regions of the received signal strength (RSS) samples which are greatly affected by reflection are divided using the k-medoids algorithm, and then the center part is clustered via density-based spatial clustering of applications with noise (DBSCAN). In the actual location estimation stage, the points to be measured can

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Bernadotte, Alexandra. "The Algorithm That Maximizes the Accuracy of k-Classification on the Set of Representatives of the k Equivalence Classes." Mathematics 10, no. 15 (2022): 2810. http://dx.doi.org/10.3390/math10152810.

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The article formulates the Dictionary Recognition problem, which is relevant for a wide range of applied problems: word recognition in a noisy audio signal for natural language processing tasks or in a noisy electromagnetic signal, recognition of visual patterns in limited visibility, and much more. A Dictionary Recognition problem is finding a set of words from a given set to maximize the classification accuracy of the words in the dictionary without losing semantic representation. The idea of solving the problem is to represent a set of objects (encoded as a sequence of symbols or visual seq

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Huang, Qihang, Yulin He, and Zhexue Huang. "A Novel Maximum Mean Discrepancy-Based Semi-Supervised Learning Algorithm." Mathematics 10, no. 1 (2021): 39. http://dx.doi.org/10.3390/math10010039.

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To provide more external knowledge for training self-supervised learning (SSL) algorithms, this paper proposes a maximum mean discrepancy-based SSL (MMD-SSL) algorithm, which trains a well-performing classifier by iteratively refining the classifier using highly confident unlabeled samples. The MMD-SSL algorithm performs three main steps. First, a multilayer perceptron (MLP) is trained based on the labeled samples and is then used to assign labels to unlabeled samples. Second, the unlabeled samples are divided into multiple groups with the k-means clustering algorithm. Third, the maximum mean

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OMRAN, M., A. P. ENGELBRECHT, and A. SALMAN. "PARTICLE SWARM OPTIMIZATION METHOD FOR IMAGE CLUSTERING." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 03 (2005): 297–321. http://dx.doi.org/10.1142/s0218001405004083.

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An image clustering method that is based on the particle swarm optimizer (PSO) is developed in this paper. The algorithm finds the centroids of a user specified number of clusters, where each cluster groups together with similar image primitives. To illustrate its wide applicability, the proposed image classifier has been applied to synthetic, MRI and satellite images. Experimental results show that the PSO image classifier performs better than state-of-the-art image classifiers (namely, K-means, Fuzzy C-means, K-Harmonic means and Genetic Algorithms) in all measured criteria. The influence of

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Mansor, Muhammad Naufal, Ahmad Kadri Junoh, Wan Suhana Wan Daud, Wan Zuki Azman Wan Muhamad, and Azrini Idris. "Nonlinear Fuzzy Robust PCA Algorithm for Pain Decision Support System." Advanced Materials Research 1016 (August 2014): 785–89. http://dx.doi.org/10.4028/www.scientific.net/amr.1016.785.

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This paper describes particular pain events to be located as in infant face images with feature extraction algorithm. Nonlinear Fuzzy Robust PCA (NFRPCA) feature extraction is implemented to test its effectiveness in recognizing pain in images. In this work, two classifiers, Fuzzy k-nearest neighbors (Fuzzy k-NN) and k-nearest neighbors (k-NN) are employed. Result shows that the NFRPCA and classifier (Fuzzy k-NN and k-NN) can be used for the recognition of infant pain images with the best accuracy of 89.77%.

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K, Venkatachalam, and Karthikeyan NK. "Effective Feature Set Selection and Centroid Classifier Algorithm for Web Services Discovery." Indonesian Journal of Electrical Engineering and Computer Science 5, no. 2 (2017): 441. http://dx.doi.org/10.11591/ijeecs.v5.i2.pp441-450.

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<p>Text preprocessing and document classification plays a vital role in web services discovery. Nearest centroid classifiers were mostly employed in high-dimensional application including genomics. Feature selection is a major problem in all classifiers and in this paper we propose to use an effective feature selection procedure followed by web services discovery through Centroid classifier algorithm. The task here in this problem statement is to effectively assign a document to one or more classes. Besides being simple and robust, the centroid classifier s not effectively used for docum

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A. Alharbi, Lubna. "Heart Disease Prediction of Cleveland Clinic Patients using Advanced Machine Learning Algorithms." Journal of Advanced Research Design 126, no. 1 (2025): 1–14. https://doi.org/10.37934/ard.126.1.114.

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Globally, cardiovascular diseases (CVDs) constitute the primary cause of morbidity and mortality worldwide. Early diagnosis of those at risk of CVDs may lower the number of avoidable fatalities. It has been shown that machine learning (ML) is helpful in anticipating cardiac issues. Adoption of a prediction system that can detect cardiac diseases before they deteriorate would offer people worldwide enormous hope and help in decision-making. ML has become a popular technique for generating predictions from enormous real-world datasets. It has also been discovered that many ML classifiers contain

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Finki Dona Marleny and Mambang. "COMPARISON OF K-NN AND NAÏVE BAYES CLASSIFIER FOR ASPHYXIA FACTOR." Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) 3, no. 1 (2018): 13–17. http://dx.doi.org/10.20527/jtiulm.v3i1.23.

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Asphyxia is influenced by several factors, including the factors affecting the Immediate Was maternal factors That relates Conditions mother Pregnancy and childbirth such as hypoxia mother, Asphyxia factor data can be modeled using the classification approach. this paper will be compared k-nearest neighbor algorithm and Naive Bayes classifier to classify asphyxia factor. Naive Bayes uses the concept of Bayes’ Theorem which assuming the independency between predictors. Basically, Bayes theorem is used to compute the subsequent probabilities. Analysis of the two algorithms has been done on sever

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Onyezewe, Anozie, Armand F. Kana, Fatimah B. Abdullahi, and Aminu O. Abdulsalami. "An Enhanced Adaptive k-Nearest Neighbor Classifier Using Simulated Annealing." International Journal of Intelligent Systems and Applications 13, no. 1 (2021): 34–44. http://dx.doi.org/10.5815/ijisa.2021.01.03.

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The k-Nearest Neighbor classifier is a non-complex and widely applied data classification algorithm which does well in real-world applications. The overall classification accuracy of the k-Nearest Neighbor algorithm largely depends on the choice of the number of nearest neighbors(k). The use of a constant k value does not always yield the best solutions especially for real-world datasets with an irregular class and density distribution of data points as it totally ignores the class and density distribution of a test point’s k-environment or neighborhood. A resolution to this problem is to dyna

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Alarbi, Abdalraouf, and Zafer Albayrak. "Core Classifier Algorithm: A Hybrid Classification Algorithm Based on Class Core and Clustering." Applied Sciences 12, no. 7 (2022): 3524. http://dx.doi.org/10.3390/app12073524.

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Machine learning classification algorithms vary drastically in their approaches, and researchers have always been trying to reduce the common boundaries of nonlinear classification, overlapping, or noise. This study summarizes the steps of hybridizing a new algorithm named Core Classify Algorithm (CCA) derived from K-nearest neighbor (KNN) and an unsupervised learning partitioning algorithm (K-means), aiming to avoid the unrepresentative Cores of the clusters while finding the similarities. This hybridization step is meant to harvest the benefits of combining two algorithms by changing results

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Bhardwaj, Shreya. "EMPLOYMENT OF THE SELECTIVE MACHINE LEARNING ALGORITHM FOR THE EARLY AND EFFECTIVE DETECTION AND DIAGNOSIS OF CARDIOVASCULAR DISEASE." International Journal of Research in Medical Sciences and Technology 11, no. 01 (2021): 239–46. http://dx.doi.org/10.37648/ijrmst.v11i01.024.

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Heart and blood artery dysfunction is the root cause of cardiovascular disease, which includes coronary heart disease, cerebrovascular disease, peripheral arterial disease, rheumatic heart disease, deep vein thrombosis, and pulmonary embolism. A model for using a machine-learning algorithm to find cardiovascular diseases is presented in this paper. Utilized the agile methodology in this research, planning, requirements analysis, designing, coding, testing, and documentation are all carried out simultaneously throughout the stages of the production process. Using four distinct machine learning

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Hind Ibrahim Mohammed, Bashar Ahmed Sultan, and Khalid Hadi Hamee. "Hand gestures recognition classification." International Journal of Engineering Research Updates 3, no. 2 (2022): 008–12. http://dx.doi.org/10.53430/ijeru.2022.3.2.0053.

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The use of hand gestures for human-machine interaction offers an enticing alternative to bulky interface devices. The current study discusses the classification of gestures in real time and aims to create an algorithm capable of classifying gestural control commands accurately. For the classification of a gesture vocabulary of eight dynamic hand gestures, two separate classifiers were created. The established classifiers were: K-means + rule-based classifier and classifier of to test the accuracy of classification recognition in which a test set of 180 trajectories was categorized, an experime

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Priyadarshini, Rojalina, Rabindra Kumar Barik, Nilamadhab Dash, Brojo Kishore Mishra, and Rachita Misra. "A Hybrid GSA-K-Mean Classifier Algorithm to Predict Diabetes Mellitus." International Journal of Applied Metaheuristic Computing 8, no. 4 (2017): 99–112. http://dx.doi.org/10.4018/ijamc.2017100106.

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Lots of research has been carried out globally to design a machine classifier which could predict it from some physical and bio-medical parameters. In this work a hybrid machine learning classifier has been proposed to design an artificial predictor to correctly classify diabetic and non-diabetic people. The classifier is an amalgamation of the widely used K-means algorithm and Gravitational search algorithm (GSA). GSA has been used as an optimization tool which will compute the best centroids from the two classes of training data; the positive class (who are diabetic) and negative class (who

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Saputro, Meidika Bagus, and Alamsyah Alamsyah. "Comparison of Naive Bayes Classifier and K-Nearest Neighbor Algorithms with Information Gain and Adaptive Boosting for Sentiment Analysis of Spotify App Reviews." Recursive Journal of Informatics 2, no. 1 (2024): 37–44. http://dx.doi.org/10.15294/rji.v2i1.68551.

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Abstract. At this time, the development of technology are increase rapidly. One of the issue that appear with advance technology is data volume in the world has increase too. With the large data volumes that exist in the world it can be used to some purpose in many field. Entertainment is one of the field that have many interest from user in this world. Spotify is the example of entertainment apps that provided by Google Play Store to give online music streams to their users. Because that apps is provided by Google Play Store, many reviews of the user about the apps it can be classified to kno

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Onan, Aytug. "Hybrid supervised clustering based ensemble scheme for text classification." Kybernetes 46, no. 2 (2017): 330–48. http://dx.doi.org/10.1108/k-10-2016-0300.

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Purpose The immense quantity of available unstructured text documents serve as one of the largest source of information. Text classification can be an essential task for many purposes in information retrieval, such as document organization, text filtering and sentiment analysis. Ensemble learning has been extensively studied to construct efficient text classification schemes with higher predictive performance and generalization ability. The purpose of this paper is to provide diversity among the classification algorithms of ensemble, which is a key issue in the ensemble design. Design/methodol

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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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GIL-PITA, ROBERTO, and XIN YAO. "EVOLVING EDITED k-NEAREST NEIGHBOR CLASSIFIERS." International Journal of Neural Systems 18, no. 06 (2008): 459–67. http://dx.doi.org/10.1142/s0129065708001725.

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The k-nearest neighbor method is a classifier based on the evaluation of the distances to each pattern in the training set. The edited version of this method consists of the application of this classifier with a subset of the complete training set in which some of the training patterns are excluded, in order to reduce the classification error rate. In recent works, genetic algorithms have been successfully applied to determine which patterns must be included in the edited subset. In this paper we propose a novel implementation of a genetic algorithm for designing edited k-nearest neighbor clas

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Husin, Abdullah, and Ku Ruhana Ku-Mahamud. "Ant System and Weighted Voting Method for Multiple Classifier Systems." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (2018): 4705. http://dx.doi.org/10.11591/ijece.v8i6.pp4705-4712.

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Combining multiple classifiers is considered as a general solution for classification tasks. However, there are two problems in combining multiple classifiers: constructing a diverse classifier ensemble; and, constructing an appropriate combiner. In this study, an improved multiple classifier combination scheme is propose. A diverse classifier ensemble is constructed by training them with different feature set partitions. The ant system-based algorithm is used to form the optimal feature set partitions. Weighted voting is used to combine the classifiers’ outputs by considering the strength of

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Husin, Abdullah, and Ku Ruhana Ku-Mahamud. "Ant System and Weighted Voting Method for Multiple Classifier Systems." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (2018): 4705–12. https://doi.org/10.11591/ijece.v8i6.pp4705-4712.

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Combining multiple classifiers is considered as a general solution for classification tasks. However, there are two problems in combining multiple classifiers: constructing a diverse classifier ensemble; and, constructing an appropriate combiner. In this study, an improved multiple classifier combination scheme is propose. A diverse classifier ensemble is constructed by training them with different feature set partitions. The ant system-based algorithm is used to form the optimal feature set partitions. Weighted voting is used to combine the classifiers’ outputs by considering the streng

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Rapacz, Sylwia, Piotr Chołda, and Marek Natkaniec. "A Method for Fast Selection of Machine-Learning Classifiers for Spam Filtering." Electronics 10, no. 17 (2021): 2083. http://dx.doi.org/10.3390/electronics10172083.

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The paper elaborates on how text analysis influences classification—a key part of the spam-filtering process. The authors propose a multistage meta-algorithm for checking classifier performance. As a result, the algorithm allows for the fast selection of the best-performing classifiers as well as for the analysis of higher-dimensionality data. The last aspect is especially important when analyzing large datasets. The approach of cross-validation between different datasets for supervised learning is applied in the meta-algorithm. Three machine-learning methods allowing a user to classify e-mail

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