Results 21 to 30 of about 1,871,394 (264)
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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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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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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K-means** - a fast and efficient K-means algorithms
International Journal of Intelligent Information and Database Systems, 2018 K-means often converges to a local optimum. In improved versions of K-means, k-means++ is well-known for achieving a rather optimum solution with its cluster initialisation strategy and high computational efficiency. Incremental K-means is recognised for its converging to the empirically global optimum but having a high complexity due to its stepping ...
Cuong Duc Nguyen, Trong Hai Duong
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$k$-means clustering of extremes [PDF]
Electronic Journal of Statistics, 2020 The $k$-means clustering algorithm and its variant, the spherical $k$-means clustering, are among the most important and popular methods in unsupervised learning and pattern detection. In this paper, we explore how the spherical $k$-means algorithm can be applied in the analysis of only the extremal observations from a data set.
Janßen, Anja, Wan, Phyllis
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The comparative study of text documents clustering algorithms
Environment Conservation Journal, 2015 Clustering is one of the most significant research area in the field of data mining and considered as an important tool in the fast developing information explosion era.Clustering systems are used more and more often in text mining, especially in ...
Mohammad Eiman Jamnezhad, Reza Fattahi
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Analyze the Clustering and Predicting Results of Palm Oil Production in Aceh Utara
IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2023 PT. Perkebunan Nusantara 1 is engaged in oil palm production with a total land area of 1,144 Ha. The formulation of this research can determine productive land clusters based on land area, number of trees, number of stages, and palm oil production ...
Mutammimul Ula +3 more
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Using classification and K-means methods to predict breast cancer recurrence in gene expression data
Journal of Medical Signals and Sensors, 2022 Background: Breast cancer is a type of cancer that starts in the breast tissue and affects about 10% of women at different stages of their lives.
Mohammadreza Sehhati +3 more
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Advances in Neural Information Processing Systems 36, 2023 The $k$-means++ algorithm of Arthur and Vassilvitskii (SODA 2007) is often the practitioners' choice algorithm for optimizing the popular $k$-means clustering objective and is known to give an $O(\log k)$-approximation in expectation. To obtain higher quality solutions, Lattanzi and Sohler (ICML 2019) proposed augmenting $k$-means++ with $O(k \log \log
Lorenzo Beretta 0001 +3 more
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