Results 21 to 30 of about 938,196 (259)
Metode Elbow dan K-Means Guna Mengukur Kesiapan Siswa SMK Dalam Ujian Nasional
Jurnal Teknologi dan Sistem Informasi, 2020 Keberhasilan siswa dalam menempuh ujian nasional (UN) dapat terlihat dari perolehan nilai mata pelajaran yang diujikan, tiga diantaranya adalah nilai matematika, Bahasa Indonesia, dan Bahasa Inggris.
Ninik Tri Hartanti
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Differentially Private K-Means Clustering [PDF]
Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy, 2016 There are two broad approaches for differentially private data analysis. The interactive approach aims at developing customized differentially private algorithms for various data mining tasks. The non-interactive approach aims at developing differentially private algorithms that can output a synopsis of the input dataset, which can then be used to ...
Su, Dong +4 more
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Penerapan Algoritma K-Means Clustering untuk Pengelompokkan Penyebaran Diare di Kabupaten Langkat [PDF]
, 2016 Diare merupakan penyakit yang bertanggung jawab untuk sekitar seperempat dari 130.000 kematian tahunan diantara anak Balita, terutama pada musim pancaroba seperti yang terjadi dihampir seluruh kawasan Indonesia tidak terlebih di kabupaten langkat ...
Nasari, F. (Fina) +1 more
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, 2019 We study the problem of online clustering where a clustering algorithm has to assign a new point that arrives to one of $k$ clusters. The specific formulation we use is the $k$-means objective: At each time step the algorithm has to maintain a set of k candidate centers and the loss incurred is the squared distance between the new point and the closest
Cohen-Addad, Vincent +3 more
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K-Means Clustering With Incomplete Data
IEEE Access, 2019 Clustering has been intensively studied in machine learning and data mining communities. Although demonstrating promising performance in various applications, most of the existing clustering algorithms cannot efficiently handle clustering tasks with ...
Siwei Wang +6 more
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Global optimality in k -means clustering [PDF]
Information Sciences, 2018 We study the problem of finding an optimum clustering, a problem known to be NP-hard. Existing literature contains algorithms running in time proportional to the number of points raised to a power that depends on the dimensionality and on the number of clusters.
Tirnauca, Cristina +3 more
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An Overlapping Subspace K-Means Clustering Algorithm [PDF]
Jisuanji gongcheng, 2020 Most of existing clustering algorithms for high-dimensional sparse data do not consider overlapping class clusters and outliers,resulting in unsatisfactory clustering results.Therefore,this paper proposes an overlapping subspace K-Means clustering ...
LIU Yuhang, MA Huifang, LIU Haijiao, YU Li
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SC3s: efficient scaling of single cell consensus clustering to millions of cells
BMC Bioinformatics, 2022 Background Today it is possible to profile the transcriptome of individual cells, and a key step in the analysis of these datasets is unsupervised clustering.
Fu Xiang Quah, Martin Hemberg
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BOOTSTRAPPING K-MEANS CLUSTERING
Journal of the Japanese Society of Computational Statistics, 1990 Summary: Independent observations \(X_ 1,X_ 2,\ldots,X_ n\) are made on a distribution \(F\) on \(R^ d\). To divide these observations into \(k\) clusters, first choose a vector of optimal cluster centers \(b_ n=(b_{n1},b_{n2},\ldots,b_{nk})\) to minimize \(W_ n(a)=n^{- 1}\sum^ n_{i=1}\min_{1\leq j\leq k}\| X_ i-a_ j\|^ 2\) as a function of \(a=(a_ 1 ...
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Clustering with Spectral Norm and the k-means Algorithm [PDF]
, 2010 There has been much progress on efficient algorithms for clustering data points generated by a mixture of $k$ probability distributions under the assumption that the means of the distributions are well-separated, i.e., the distance between the means of ...
Kannan, Ravindran, Kumar, Amit
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