pmclust v0.2-0
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Parallel Model-Based Clustering using Expectation-Gathering-Maximization Algorithm for Finite Mixture Gaussian Model
Aims to utilize model-based clustering (unsupervised)
for high dimensional and ultra large data, especially in a distributed
manner. The code employs 'pbdMPI' to perform a
expectation-gathering-maximization algorithm
for finite mixture Gaussian
models. The unstructured dispersion matrices are assumed in the
Gaussian models. The implementation is default in the single program
multiple data programming model. The code can be executed
through 'pbdMPI' and MPI' implementations such as 'OpenMPI'
and 'MPICH'.
See the High Performance Statistical Computing website
<https://snoweye.github.io/hpsc/>
for more information, documents and examples.
Functions in pmclust
| Name | Description | |
| One E-Step | Compute One E-step and Log Likelihood Based on Current Parameters | |
| generate.basic | Generate Examples for Testing | |
| Set of PARAM | A Set of Parameters in Model-Based Clustering. | |
| Internal Functions | All Internal Functions | |
| One Step of EM algorithm | One EM Step for GBD | |
| One M-Step | Compute One M-Step Based on Current Posterior Probabilities | |
| assign.N.sample | Obtain a Set of Random Samples for X.spmd | |
| Independent logL | Independent Function for Log Likelihood | |
| pmclust-package | Parallel Model-Based Clustering | |
| Update Class of EM or Kmenas Results | Update CLASS.spmd Based on the Final Iteration | |
| Set of CONTROL | A Set of Controls in Model-Based Clustering. | |
| mb.print | Print Results of Model-Based Clustering | |
| pmclust and pkmeans | Parallel Model-Based Clustering and Parallel K-means Algorithm | |
| print.object | Functions for Printing or Summarizing Objects According to Classes | |
| as functions | Convert between X.gbd (X.spmd) and X.dmat | |
| get.N.CLASS | Obtain Total Elements for Every Clusters | |
| Initialization | Initialization for EM-like Algorithms | |
| EM-like algorithms | EM-like Steps for GBD | |
| Read Me First | Read Me First Function | |
| Set Global Variables | Set Global Variables According to the global matrix X.gbd (X.spmd) or X.dmat | |
| generate.MixSim | Generate MixSim Examples for Testing | |
| No Results! | ||
Vignettes of pmclust
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Details
| Date | 2018-02-01 |
| LazyLoad | yes |
| LazyData | yes |
| License | GPL (>= 2) |
| URL | http://r-pbd.org/ |
| BugReports | http://group.r-pbd.org/ |
| MailingList | Please send questions and comments regarding pbdR to RBigData@gmail.com |
| NeedsCompilation | yes |
| Packaged | 2018-02-02 04:12:21 UTC; snoweye |
| Repository | CRAN |
| Date/Publication | 2018-02-02 04:41:01 UTC |
| imports | MASS , methods |
| enhances | MixSim |
| depends | pbdBASE (>= 0.4-3) , pbdDMAT (>= 0.4-0) , pbdMPI (>= 0.3-1) , R (>= 3.0.0) |
| Contributors | George Ostrouchov |
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