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147 results for “Clustering Method”
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Research ArticleOpen accessIn the keywordsPredicting Missing Attribute Values Using k-Means ClusteringNambiraj Suguna, Keppana Gowder ThanushkodiProblem statement: Predicting the value for missing attributes is an important data preprocessing problem in data mining and knowledge discovery tasks. Several methods have been proposed to treat missing data and the one used more frequently is deleting instances containing at least one missing value of a feature. When the dataset has minimum number of missing attribute values then we … …Journal of Computer Science, Vol. 7, Issue 2 · 25 Feb 2011 · 7,873 accessesView PDFCite
Research ArticleOpen accessIn the keywordsSELECTING THE COVARIANCE STRUCTURE IN MIXED MODEL USING STATISTICAL METHODS CALIBRATIONAli Hussein AL-MarshadiIn this article the analysis of experiment of repeated measures design is considered which is used often in different fields of studies. In order to analyze the experiment of repeated measures design efficiently we need to select the suitable covariance structure which required a lot of efforts. In the current paper an approach is used to guide the selection of … …Journal of Mathematics and Statistics, Vol. 10, Issue 3 · 8 Jul 2014 · 7,222 accessesView PDFCite
Research ArticleOpen accessIn the keywordsApplication of Statistical Procedures for Analysis of Genetic Diversity in Domestic Animal PopulationsM. R. Nassiry, A. Javanmard, Reza TohidiProblem statement: A wide range of studies for the assessment of genetic diversity in livestock breed were conducted using genetic distance. For high-accuracy and unbiased estimation sampling methods, criteria of choosing type of DNA markers, distance measurement strategies, cluster analysis will be important for any genetic diversity projects. Approach: Main objective of this short review is focusing on application statistical … …American Journal of Animal and Veterinary Sciences, Vol. 4, Issue 4 · 31 Dec 2009 · 7,646 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Adaptive Detail Preserving Modified Progressive Pca Thresholding Based Image DenoisingN. Anilkumar, S. Radhakrishnan, Shaik Syed Basha et al.Radiologists use medical imaging tools to assess the anatomy and function of interior organs. However, medical images are frequently distorted by various types of noise generated during the capture, transmission, and reconstruction operations, which can dramatically reduce image quality and hide critical diagnostic features. As a result, developing effective denoising algorithms that suppress noise while maintaining structural information remains a … …Journal of Computer Science, Vol. 22, Issue 8 · 8 Sep 2026 · 529 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnhanced Lifetime Maximization Using Metaheuristics-Based Unequal Clustering With Routing Protocol on Wireless Sensor NetworksN. E. Kanaghadhara, R. SubanThe Wireless Sensor Network (WSN) consists of spatially distributed autonomous sensors that monitor environmental or physical factors such as sound, pressure, or temperature, and pass the information directly through the network to a sink or central location. Clustering and routing are essential in WSNs to optimize their longevity, scalability, and efficiency. Clustering includes grouping Sensor Nodes (SN) into clusters, each … …Journal of Computer Science, Vol. 22, Issue 7 · 21 Jul 2026 · 682 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Sentiment-Aware Hybrid Ensemble Clustering Framework for Social Network Content Analysis and Community DiscoveryDhimesh Pravinbhai Parmar, Paresh TannaSocial media stages produce huge volumes of short, familiar, and sentiment-rich content that offerings significant encounter for community discovery and thematic analysis. Traditional clustering approach struggle with sparsity, noise, and limited surroundings information present in such texts. To address these limitations, this study introduces a sentiment-aware hybrid ensemble clustering framework designed openly for social network content analysis. The planned method … …Journal of Computer Science, Vol. 22, Issue 5 · 26 May 2026 · 1,271 accessesView PDFCite
Research ArticleOpen accessIn the abstractImproving Clustering Robustness through Fuzzy Ensemble of K-Means and Mean ShiftLNC. Prakash K., Palamakula Ramesh Babu, Shaik Thaseentaj et al.Ensemble clustering has emerged as a powerful strategy to improve the robustness and accuracy of unsupervised learning, particularly when individual algorithms struggle with noisy, heterogeneous, or high-dimensional data. This study introduces a fuzzy-based ensemble approach that integrates the complementary strengths of K-Means and Mean Shift clustering, followed by fuzzy membership assignment for data points that remain ambiguous. The inclusion of … …Journal of Computer Science, Vol. 22, Issue 5 · 20 May 2026 · 1,257 accessesView PDFCite
Research ArticleOpen accessIn the abstractOptimized Wireless Sensor Network Clustering Using a Hybrid Firefly–Bat Algorithm (HFBA)J. Jegan, Senthil kumar P, S. Saravanakumar et al.Wireless Sensor Networks (WSNs) are composed of numerous sensor nodes that collaborate to observe and evaluate various environmental parameters, transmitting the data to a central base station. Each node is equipped with a unique transmitter, receiver, and processor to facilitate sensing, processing, and communication. These networks are often installed in extremely remote and inaccessible areas. A significant challenge arises from … …Journal of Computer Science, Vol. 22, Issue 5 · 19 May 2026 · 1,431 accessesView PDFCite
Review ArticleOpen accessIn the abstractImproving Classification and Clustering Accuracy of Palembang Duku Fruit Quality via CNN-K-Means IntegrationHenny Hartono, Francka Sakti Lee, Herlina Herlina et al.Duku Palembang (Lansium domesticum Corr.) is a leading commodity from South Sumatra with high economic value. The quality assessment process for duku has traditionally relied on manual visual inspection, which is subjective, time-consuming, and inconsistent, especially at large production scales. This study proposes an integrative framework combining Convolutional Neural Network (CNN) and K-Means Clustering to improve the accuracy of classification … …OnLine Journal of Biological Sciences, Vol. 26, Issue 2 · 8 May 2026 · 1,475 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Detailed Preserving Medical Image Denoising Using Cluster-Wise PCA Thresholding and Iterative Mean FilteringMohit Sharma, Ayush Dogra, Anita Gupta et al.Image denoising is a vital process in medical imaging that involves removing noise or distortions introduced during image acquisition. Random noise can degrade image quality and reduce contrast, making accurate analysis more challenging. This paper presents a detail-preserving medical image denoising framework based on a hybrid methodology that combines cluster-wise Principal Component Analysis (PCA) thresholding with Iterative Mean Filtering (IMF). … …Journal of Computer Science, Vol. 22, Issue 4 · 18 Apr 2026 · 1,787 accessesView PDFCite