mcap: mcap: Model-based clustering in very high dimensions via...
In btaschler/mcap: Model-Based Clustering in High Dimensions via Adaptive Projections
Description
Main mcap functions
Description
Model-based clustering in very high dimensions (especially p >> n) via adaptive projections.
Clustering is based on Gaussian mixture models in a lower dimensional (projected) space.
Projection dimension is set adaptively based on a cluster stability criterion.
Available projection variants (so far) include PCA and Random Projections.
Main mcap functions
"MCAPfit"
"GMMwrapper"
"OptDimClusterStability"
"ClusterStability"
"GramPCA"
btaschler/mcap documentation built on May 26, 2019, 1:31 a.m.
R Package Documentation
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Description Main mcap functions
Description
Model-based clustering in very high dimensions (especially p >> n) via adaptive projections. Clustering is based on Gaussian mixture models in a lower dimensional (projected) space. Projection dimension is set adaptively based on a cluster stability criterion. Available projection variants (so far) include PCA and Random Projections.
Main mcap functions
"MCAPfit"
"GMMwrapper"
"OptDimClusterStability"
"ClusterStability"
"GramPCA"
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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