| Type: | Package |
| Title: | Clustering via Stochastic Approximation and Gaussian Mixture Models |
| Version: | 0.2.4 |
| Date: | 2019-06-29 |
| Author: | Andrew T. Jones, Hien D. Nguyen |
| Maintainer: | Andrew T. Jones <andrewthomasjones@gmail.com> |
| Description: | Computes clustering by fitting Gaussian mixture models (GMM) via stochastic approximation following the methods of Nguyen and Jones (2018) <doi:10.1201/9780429446177>. It also provides some test data generation and plotting functionality to assist with this process. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| Imports: | Rcpp (≥ 0.12.13), MixSim, mclust, stats, lowmemtkmeans |
| LinkingTo: | Rcpp, RcppArmadillo |
| RoxygenNote: | 6.1.1 |
| Suggests: | testthat, ggplot2 |
| NeedsCompilation: | yes |
| Packaged: | 2019-06-29 05:30:02 UTC; andrewjones |
| Repository: | CRAN |
| Date/Publication: | 2019-06-29 05:50:12 UTC |
SAGMM: A package for Clustering via Stochastic Approximation and Gaussian Mixture Models.
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.
Clustering via Stochastic Approximation and Gaussian Mixture Models (GMM)
Description
Fit a GMM via Stochastic Approximation. See Reference.
Usage
SAGMMFit(X, Y = NULL, Burnin = 5, ngroups = 5, kstart = 10,
plot = FALSE)
Arguments
X |
numeric matrix of the data. |
Y |
Group membership (if known). Where groups are integers in 1:ngroups. If provided ngroups can |
Burnin |
Ratio of observations to use as a burn in before algorithm begins. |
ngroups |
Number of mixture components. If Y is provided, and groups is not then is overridden by Y. |
kstart |
number of kmeans starts to initialise. |
plot |
If TRUE generates a plot of the clustering. |
Value
A list containing
Cluster |
The clustering of each observation. |
plot |
A plot of the clustering (if requested). |
l2 |
Estimate of Lambda^2 |
ARI1 |
Adjusted Rand Index 1 - using k-means |
ARI2 |
Adjusted Rand Index 2 - using GMM Clusters |
ARI3 |
Adjusted Rand Index 3 - using intialiation k-means |
KM |
Initial K-means clustering of the data. |
pi |
The cluster proportions (vector of length ngroups) |
tau |
tau matrix of conditional probabilities. |
fit |
Full output details from inner C++ loop. |
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.
Examples
sims<-generateSimData(ngroups=10, Dimensions=10, Number=10^4)
res1<-SAGMMFit(sims$X, sims$Y)
res2<-SAGMMFit(sims$X, ngroups=5)
Return Gamma, a sequence of gain factors
Description
Generate a series of gain factors.
Usage
gainFactors(Number, Burnin)
Arguments
Number |
Number of values required. |
Burnin |
Number of 'Burnin' values at the beginning of sequence. |
Value
Gamma, a vector of gain factors.
Examples
g<-gainFactors(10^4, 2*10^3)
Generate data for simulations to test the SAGMM package..
Description
This function is primarily a convienence wrapper for MixSim.
Usage
generateSimData(ngroups = 5, Dimensions = 5, Number = 10^4)
Arguments
ngroups |
Number of mixture components. Default 5. |
Dimensions |
number of Dimensions. Default 5. |
Number |
number of samples. Default 10^4. |
Value
List of results: X, Y, simobject.
Examples
sims<-generateSimData(ngroups=10, Dimensions=10, Number=10^4)
sims<-generateSimData()