A novel approach for the segmentation of retinal images by integration K-means clustering algorithm with graph cut for image segmentation. [PDF]
Sci RepBhoopalan R, Priyadharshini S.
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Location optimization of cold chain logistics parks based on Bayesian probability theory and K-means clustering analysis in China. [PDF]
Sci RepWang L, Liu X, Wang X, Jiang Y.
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Hyperspectral imaging and K-means clustering for material structure classification and detection of unmanned aerial vehicles. [PDF]
Sci RepSaber A, Mahmoud A, El-Sharkawy YH.
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Data-Driven Classification of Solubility Space in Deep Eutectic Solvents: Deciphering Driving Forces Using PCA and K-Means Clustering. [PDF]
MoleculesCysewski P, Przybyłek M, Jeliński T.
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Hybrid wavelet transform, K-means singular value decomposition and Spatial memory guided cat swarm optimization technique for watermark embedding. [PDF]
Sci RepSharma C +5 more
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Smart defense based on explainable stacked machine learning architecture for securing internet of health things with K-means clustering. [PDF]
Sci RepSaba T +5 more
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A fundamental limitation of quantization techniques like the k-means clustering algorithm is the storage and run-time cost associated with the large numbers of clusters required to keep quantization errors small and model fidelity high. We develop new models with a compositional parameterization of cluster centers, so representational capacity ...
Mohammad Norouzi 0002, David J. Fleet
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K-Means is a very common method of unsupervised learning in data mining. It is introduced by Steinhaus in 1956. As time flies, many other enhanced methods of k-Means have been introduced and applied. One of the significant characteristic of k-Means is randomize.
Chen-Ling Tai, Chen-Shu Wang
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K*-Means: An Effective and Efficient K-Means Clustering Algorithm
2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom), 2016K-means is a widely used clustering algorithm in field of data mining across different disciplines in the past fifty years. However, k-means heavily depends on the position of initial centers, and the chosen starting centers randomly may lead to poor quality of clustering.
Jianpeng Qi +3 more
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PERFORMANCE COMPARISON OF K-MEANS, PARALLEL K-MEANS AND K-MEANS++
K-means clustering is a fundamental unsupervised machine learning technique widely applied in various domains such as data analysis, pattern recognition, and clustering-based tasks. However, its efficiency and scalability can be challenged, particularly when dealing with large-scale datasets and complex data structures.Aliguliyev, Ramiz, Shalala F. Tahirzada
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