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tglkmeans
View on CRAN: Click
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Download and install tglkmeans package within the R console
Install from CRAN:
install.packages("tglkmeans")
Install from Github:
library("remotes")
install_github("cran/tglkmeans")
Install by package version:
library("remotes")
install_version("tglkmeans", "0.5.5")
Attach the package and use:
library("tglkmeans")
Maintained by
Aviezer Lifshitz
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First Published: 2022-04-20
Latest Update: 2024-01-09
Description:
Efficient implementation of K-Means++ algorithm. For more information see (1) "kmeans++ the advantages of the k-means++ algorithm" by David Arthur and Sergei Vassilvitskii (2007), Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, pp. 1027-1035, and (2) "The Effectiveness of Lloyd-Type Methods for the k-Means Problem" by Rafail Ostrovsky, Yuval Rabani, Leonard J. Schulman and Chaitanya Swamy <doi:10.1145/2395116.2395117>.
How to cite:
Aviezer Lifshitz (2022). tglkmeans: Efficient Implementation of K-Means++ Algorithm. R package version 0.5.5, https://cran.r-project.org/web/packages/tglkmeans. Accessed 27 Jan. 2025.
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Complete documentation for tglkmeans
Functions, R codes and Examples using
the tglkmeans R package
Some associated functions: TGL_kmeans . TGL_kmeans_tidy . downsample_matrix . pipe . simulate_data . tglkmeans-package . tglkmeans.set_parallel .
Some associated R codes: RcppExports.R . TGL_kmeans.R . downsample.R . intra_clustering.R . misc.R . tests.R . tglkmeans-package.R . utils-pipe.R . zzz.R . Full tglkmeans package functions and examples
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