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GMKMcharlie
View on CRAN: Click
here
Download and install GMKMcharlie package within the R console
Install from CRAN:
install.packages("GMKMcharlie")
Install from Github:
library("remotes")
install_github("cran/GMKMcharlie")
Install by package version:
library("remotes")
install_version("GMKMcharlie", "1.1.5")
Attach the package and use:
library("GMKMcharlie")
Maintained by
Charlie Wusuo Liu
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-10-08
Latest Update: 2021-05-29
Description:
High performance trainers for parameterizing and clustering weighted data. The Gaussian mixture (GM) module includes the conventional EM (expectation maximization) trainer, the component-wise EM trainer, the minimum-message-length EM trainer by Figueiredo and Jain (2002) . These trainers accept additional constraints on mixture weights, covariance eigen ratios and on which mixture components are subject to update. The K-means (KM) module offers clustering with the options of (i) deterministic and stochastic K-means++ initializations, (ii) upper bounds on cluster weights (sizes), (iii) Minkowski distances, (iv) cosine dissimilarity, (v) dense and sparse representation of data input. The package improved the typical implementations of GM and KM algorithms in various aspects. It is carefully crafted in multithreaded C++ for modeling large data for industry use.
How to cite:
Charlie Wusuo Liu (2019). GMKMcharlie: Unsupervised Gaussian Mixture and Minkowski and Spherical K-Means with Constraints. R package version 1.1.5, https://cran.r-project.org/web/packages/GMKMcharlie. Accessed 21 Nov. 2024.
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Other R packages that GMKMcharlie depends,
imports, suggests or enhances
Complete documentation for GMKMcharlie
Functions, R codes and Examples using
the GMKMcharlie R package
Some associated functions: GM . GMcw . GMfj . KM . KMconstrained . KMconstrainedSparse . KMppIni . KMppIniSparse . KMsparse . d2s . s2d .
Some associated R codes: RcppExports.R . Full GMKMcharlie package functions and examples
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