ganDataModel: Create a Hierarchical, Categorical Data Model for a Data Source
Neural networks are applied to create a density value function which approximates density values for a data source. The trained neural network is analysed for different levels. For each level subspaces with density values above a level are determined. The obtained set of subspaces categorizes the data source hierarchically. A prerequisite is the definition of a data source, the generation of generative data and the calculation of density values. These tasks are executed using package 'ganGenerativeData' <https://cran.r-project.org/package=ganGenerativeData>.
| Version: |
1.0.2 |
| Imports: |
Rcpp (≥ 1.0.3), tensorflow (≥ 2.0.0) |
| LinkingTo: |
Rcpp |
| Published: |
2022-07-16 |
| Author: |
Werner Mueller |
| Maintainer: |
Werner Mueller <werner.mueller5 at chello.at> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
yes |
| SystemRequirements: |
TensorFlow (https://www.tensorflow.org) |
| CRAN checks: |
ganDataModel results |
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