Results 31 to 40 of about 942,452 (277)
Initializing k-means Clustering
Proceedings of the 9th International Conference on Data Science, Technology and Applications, 2020 The quality of clustering results obtained with the k-means algorithm depends heavily on the initialization of the cluster centers. Simply sampling centers uniformly at random from the data points usually yields fairly poor and unstable results. Hence several alternatives have been suggested in the past, among which Maximin (Hathaway et al., 2006) and ...
Borgelt, Christian, Yarikova, Olha
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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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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.
Cristina Tîrnauca +3 more
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Merging $K$-means with hierarchical clustering for identifying general-shaped groups [PDF]
, 2017 Clustering partitions a dataset such that observations placed together in a group are similar but different from those in other groups. Hierarchical and $K$-means clustering are two approaches but have different strengths and weaknesses.
Ghosh, Arka P. +2 more
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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 ...
Dong Su +4 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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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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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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Unsupervised Multi-View K-Means Clustering Algorithm
IEEE Access, 2023 Since advanced technologies via social media, internet, virtual communities and networks and internet of things (IoT), there are more multi-view data to be collected.
Miin-Shen Yang, Ishtiaq Hussain
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Evidence accumulation clustering using combinations of features
MethodsX, 2020 : Evidence accumulation clustering (EAC) is an ensemble clustering algorithm that can cluster data for arbitrary shapes and numbers of clusters. Here, we present a variant of EAC in which we aimed to better cluster data with a large number of features ...
William Wong, Naotsugu Tsuchiya
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