Description Arguments Details Value References Examples

The ESOM (emergent self organizing map) algorithm as defined by [Ultsch 1999]. A set of weigths(neurons) on a two-dimensional grid get trained to adapt the given datastructure. The weights will be used to project data on a two-dimensional space, by seeking the BestMatches for every datapoint.

`Data` |
Data that will be used for training and projection |

`Lines` |
Height of grid |

`Columns` |
Width of grid |

`Epochs` |
Number of Epochs the ESOM will run |

`Toroid` |
If TRUE, the grid will be toroid |

`NeighbourhoodFunction` |
Type of Neighbourhood; Possible values are: "cone", "mexicanhat" and "gauss" |

`StartLearningRate` |
Initial value for LearningRate |

`EndLearningRate` |
Final value for LearningRate |

`StartRadius` |
Start value for the Radius in which will be searched for neighbours |

`EndRadius` |
End value for the Radius in which will be searched for neighbours |

`NeighbourhoodCooling` |
Cooling method for radius; "linear" is the only available option at the moment |

`LearningRateCooling` |
Cooling method for LearningRate; "linear" is the only available option at the moment |

`shinyProgress` |
Generate progress output for shiny if Progress Object is given |

`ShiftToHighestDensity` |
If True, the Umatrix will be shifted so that the point with highest density will be at the center |

`InitMethod` |
name of the method that will be used to choose initializations Valid Inputs: "uni_min_max": uniform distribution with minimum and maximum from sampleData "norm_mean_2std": normal distribuation based on mean and standard deviation of sampleData |

`Key` |
Vector of numeric keys matching the datapoints. Will be added to Bestmatches |

`UmatrixForEsom` |
If TRUE, Umatrix based on resulting ESOM is calculated and returned |

On a toroid grid, opposing borders are connected.

List with

`BestMatches` |
BestMatches of datapoints |

`Weights` |
Trained weights |

`Lines` |
Height of grid |

`Columns` |
Width of grid |

`Toroid` |
TRUE if grid is a toroid |

Kohonen, T., Self-organized formation of topologically correct feature maps. Biological cybernetics, 1982. 43(1): p. 59-69.

Ultsch, A., Data mining and knowledge discovery with emergent self-organizing feature maps for multivariate time series. Kohonen maps, 1999. 46: p. 33-46.

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```
[1] "Epoch: 1 started"
[1] "Epoch: 2 started"
[1] "Epoch: 3 started"
[1] "Epoch: 4 started"
[1] "Epoch: 5 started"
[1] "Epoch: 6 started"
[1] "Epoch: 7 started"
[1] "Epoch: 8 started"
[1] "Epoch: 9 started"
[1] "Epoch: 10 started"
[1] "Epoch: 11 started"
[1] "Epoch: 12 started"
[1] "Epoch: 13 started"
[1] "Epoch: 14 started"
[1] "Epoch: 15 started"
[1] "Epoch: 16 started"
[1] "Epoch: 17 started"
[1] "Epoch: 18 started"
[1] "Epoch: 19 started"
[1] "Epoch: 20 started"
[1] "Epoch: 21 started"
[1] "Epoch: 22 started"
[1] "Epoch: 23 started"
[1] "Epoch: 24 started"
[1] "---- Esom Training Finished ----"
[1] "shift to point of highest density"
```

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