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19 results for “K-Mean Clustering”
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Research ArticleOpen accessEvery word in the titleImproving 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 accessEvery word in the titleArtificial Bee Colony Algorithm Integrated with Fuzzy C-mean Operator for Data ClusteringM. Krishnamoorthi, A. M. NatarajanClustering task aims at the unsupervised classification of patterns in different groups. To enhance the quality of results, the emerging swarm-based algorithms now-a-days become an alternative to the conventional clustering methods. In this study, an optimization method based on the swarm intelligence algorithm is proposed for the purpose of clustering. The significance of the proposed algorithm is that it uses … …Journal of Computer Science, Vol. 9, Issue 4 · 6 May 2013 · 6,299 accessesView PDFCite
Research ArticleOpen accessIn the keywordsA 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
Research ArticleOpen accessIn the keywordsDetection of Plant Leaf Diseases Using K‒mean++ Intermeans Thresholding AlgorithmKittipol Wisaeng, Worawat Sa–NgiamviboolIn the field of agricultural information, the plant leaf disease detection is highly important for both farmer life and environment. To improve the accuracy of plant leaf disease detection and reduce the image processing time, the improved K‒mean++ clustering and intermeans thresholding method are proposed in this study. The proposed algorithms are used for training and testing diseases in plant … …Journal of Computer Science, Vol. 16, Issue 9 · 21 Sep 2020 · 6,750 accessesView PDFCite
Research ArticleOpen accessIn the keywordsAn Overview of Research Challenges for Classification of Cardiotocogram DataC. Sundar, M. Chitradevi, G. GeetharamaniCardiotocography (CTG) is a simultaneous recording of Fetal Heart Rate (FHR) and Uterine Contractions (UC). The most common diagnostic techniques to evaluate maternal and fetal well-being during pregnancy and before delivery. By observing the Cardiotocography trace patterns doctors can understand the state of the fetus. There are several signal processing and computer programming based techniques for interpreting a typical Cardiotocography … …Journal of Computer Science, Vol. 9, Issue 2 · 2 Apr 2013 · 7,827 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 abstractA Fuzzy Clustering Based Method for the Spatio-Temporal Data AnalysisAnahita Zolghadr, Afsaneh JalalianSpatiotemporal data is a type of data that is collected by the sensors. This type of data has two spatial and temporal dimensions. There are many challenges in analyzing spatiotemporal big data. Common evaluation metrics of clustering methods are not appropriate for spatiotemporal data. Previous clustering methods and the conventional evaluation metrics are efficient for data like time series with … …Journal of Computer Science, Vol. 18, Issue 6 · 13 Jun 2022 · 5,638 accessesView PDFCite
Research ArticleOpen accessIn the abstractDetection of Rust Emergence in Coffee Plantations using Data Mining: A Systematic ReviewLenin Quiñones Huatangari, Candy Lisbeth Ocaña Zúñiga, Annick Estefany Huaccha Castillo et al.Hemileia vastatrix is a fungus that causes coffee rust disease and, depending on the level of severity, reduces the photosynthetic capacity of the plant and of new shoots, leading to low coffee yields and even death; its symptoms are visible on the leaf. Systems based on computer algorithms have been developed to predict diseases and pests in coffee. The objective … …OnLine Journal of Biological Sciences, Vol. 22, Issue 2 · 31 Mar 2022 · 8,022 accessesView PDFCite
Research ArticleOpen accessIn the abstractInvestigating Commuting Time in a Metropolitan Statistical Area Using Spatial Autocorrelation AnalysisS. Hessam Miri, S. Behnam MiriCommuting is an unavoidable issue as living and working are two spatially separated activities for most people. The most influence of commuting is on land uses and transportation systems and ultimately it poses its consequences to the society. Research on urban commuting is one of the most favorable approaches to lessening the impact and intensity of land use and transportation … …American Journal of Engineering and Applied Sciences, Vol. 13, Issue 1 · 11 Jan 2020 · 7,421 accessesView PDFCite
EditorialOpen accessIn the abstractData Mining in Time Series: Current Study and Future TrendTan Yan, Liudmila Ulanova, Ye Ouyang, Fengyuan XuTime series represent sequences of data points where usually their order is defined by the time when they were recorded. Thus, virtually any sequential recordings can be stored as time series: Stock prices, weather conditions, physical system parameters change, product quality monitoring, etc. This leads to ubiquity of time series in all scientific and practical fields (Esling and Agon, 2012); … …Journal of Computer Science, Vol. 10, Issue 12 · 26 Jan 2015 · 8,756 accessesView PDFCite