SAGMM: SAGMM: A package for Clustering via Stochastic Approximation...
In SAGMM: Clustering via Stochastic Approximation and Gaussian Mixture Models
Description
Author(s)
References
Description
The SAGMM package allows for computation of gaussian mixture models using stochastic approximation to increase efficiency with large data sets.
The primary function SAGMMFit allows this to be performed in a relative flexible manner.
Author(s)
Andrew T. Jones and Hien D. Nguyen
References
Nguyen & Jones (2018). Big Data-Appropriate Clustering via Stochastic Approximation and Gaussian Mixture Models. In Data Analytics (pp. 79-96). CRC Press.
SAGMM documentation built on June 29, 2019, 9:02 a.m.
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Description Author(s) References
Description
The SAGMM package allows for computation of gaussian mixture models using stochastic approximation to increase efficiency with large data sets.
The primary function SAGMMFit allows this to be performed in a relative flexible manner.
Author(s)
Andrew T. Jones and Hien D. Nguyen
References
Nguyen & Jones (2018). Big Data-Appropriate Clustering via Stochastic Approximation and Gaussian Mixture Models. In Data Analytics (pp. 79-96). CRC Press.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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