Results 21 to 30 of about 17,769,086 (225)
Optimized K‐Means Algorithm [PDF]
Mathematical Problems in Engineering, 2014 The localization of the region of interest (ROI), which contains the face, is the first step in any automatic recognition system, which is a special case of the face detection. However, face localization from input image is a challenging task due to possible variations in location, scale, pose, occlusion, illumination, facial expressions, and clutter ...
Samir Brahim Belhaouari +2 more
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Oversampling for Imbalanced Learning Based on K-Means and SMOTE [PDF]
Information Sciences, 2017 Learning from class-imbalanced data continues to be a common and challenging problem in supervised learning as standard classification algorithms are designed to handle balanced class distributions. While different strategies exist to tackle this problem,
F. Last, Georgios Douzas, F. Bação
semanticscholar +1 more source
Metode Boost-K-means untuk Clustering Puskesmas berdasarkan Persentase Bayi yang Diimunisasi
JRST: Jurnal Riset Sains dan Teknologi, 2020 Dinas Kesehatan Kabupaten/Kota adalah satuan kerja pemerintahan daerah kabupaten/kota yang bertanggung jawab menyelenggarakan urusan pemerintahan dalam bidang kesehatan di kabupaten/kota.
Ahmad Irfan Abdullah +2 more
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Kernel Probabilistic K-Means Clustering
Sensors, 2021 Kernel fuzzy c-means (KFCM) is a significantly improved version of fuzzy c-means (FCM) for processing linearly inseparable datasets. However, for fuzzification parameter m=1, the problem of KFCM (kernel fuzzy c-means) cannot be solved by Lagrangian ...
Bowen Liu +4 more
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Clustering models for hospitals in Jakarta using fuzzy c-means and k-means
Procedia Computer Science, 2023 After facing the COVID-19 pandemic, national and local governments in Indonesia realized a gap in the distribution of health care and human health practitioners.
Karli Eka Setiawan +3 more
semanticscholar +1 more source
Towards optimal lower bounds for k-median and k-means coresets [PDF]
Symposium on the Theory of Computing, 2022 The (k,z)-clustering problem consists of finding a set of k points called centers, such that the sum of distances raised to the power of z of every data point to its closest center is minimized.
Vincent Cohen-Addad +3 more
semanticscholar +1 more source
Customer analytics for online retailers using weighted k-means and RFM analysis
Data Analytics and Applied Mathematics, 2023 In recent years, there has been a significant trend toward data-driven enterprises in the business world. This trend is exemplified by the frustration reported by 74% of customers when they encounter ads that are not relevant to them, as reported by ...
Ahmed Mohamed Ahmed Serwah +3 more
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Bulletin of the History of Archaeology, 2015 New Deal archaeology survey and excavation projects across the lower 48 states exhibit considerable geographic variation in their nature and extent. Part of this variation can be linked to strong regional personalities, while other variation depended on ...
Bernard K. Means
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The Monongahela tradition in "real time": Bayesian analysis of radiocarbon dates.
PLoS ONE, 2022 Despite advances in techniques, methods, and theory, northeastern North American archaeologists continue to use early to mid-twentieth century culture historical taxa as units of analysis and narrative.
John P Hart, Bernard K Means
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Penggunaan Metode K-Means dan K-Means++ Sebagai Clustering Data Covid-19 di Pulau Jawa
Teknika, 2022 Virus Corona (Covid-19) merupakan penyakit menular yang dapat ditularkan antara hewan dan manusia. Pada akhir Desember 2019, virus itu teridentifikasi di Provinsi Wuhan, Cina.
Nursatio Nugroho, Faisal Dharma Adhinata
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