interactiveProjectionBasedClustering {ProjectionBasedClustering}

Interactive Projection-Based Clustering (IPBC)

Description

An interactive clustering tool published in [Thrun et al., 2020] that uses the topographic map visualizations of the generalized U-matrix and a variety of different projection methods. This function receives a dataset and starts a shiny interface where one is able to choose a projection method and generate a plot.ly visualization of the topograhpic map [Thrun et al., 2016] of the generalized U-matrix [Ultsch/Thrun, 2017] combined with projected points. It includes capabilities for interactive clustering within the interface as well as automatic projection-based clustering based on [Thrun/Ultsch, 2020].

Usage

  interactiveProjectionBasedClustering(Data, Cls=NULL)
  
  IPBC(Data, Cls=NULL)

Arguments

Data

The dataset [1:n,1:d] of n cases and d vriables with which the U-matrix and the projection will be calculated. Please see also the note below.

Cls

Optional: Prior Classification of the data for the [1:n] cases of k classes.

Details

To cluster data interactively, i.e., select specific data points and create a cluster), first generate the visualization. Thereafter, switch in the menu to clustering, hold the left mouse button and then frame a valley. Simple mouse clicks will not start the lasso functionality of plotly.

The resulting clustering is stored in Cls which is a numerical vector of the length n (number of cases) with the integer elements of numbers from 1 to k if k is the number of groups in the data. Each element of Cls as an unambigous mapping to a case of Data indicating by the rownames of Data. If Data has no rownames a vector from 1:n is generated and then Cls is named by it.

Value

Returns a List of:

Cls

[1:n] numerical vector of the clustering of the dataset for then cases of k clusters

Umatrix

[1:Lines,1:Columns] generalized Umatrix to be plotted, numerical matrix storing the U-heights, see [Thrun, 2018] for definition.

Bestmatches

[1:n,2] Matrix of GridConverted Projected Points [1:n, 1:2] called Bestmatches that defines positions for n datapoints, first columns is the position in Lines and second column in Columns

LastProjectionMethodUsed

name of last projection method that was used as a string

TopView_TopographicMap

The final plot generated by plot.ly when closing the tool

Note

Some dimensionality reduction methods will assume data without missing values, some other DR methods assume unique data points, i.e., no distance=0 for any two cases(rows) of data. In these cases the IPBC method will crash.

Author(s)

Tim Schreier, Felix Pape, Luis Winckelmann, Michael Thrun

References

[Ultsch/Thrun, 2017] Ultsch, A., & Thrun, M. C.: Credible Visualizations for Planar Projections, in Cottrell, M. (Ed.), 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM), IEEE Xplore, France, 2017.

[Thrun/Ultsch, 2017] Thrun, M. C., & Ultsch, A. : Projection based Clustering, Proc. International Federation of Classification Societies (IFCS), pp. 250-251, Japanese Classification Society (JCS), Tokyo, Japan, 2017.

[Thrun/Ultsch, 2020] Thrun, M. C., & Ultsch, A.: Using Projection based Clustering to Find Distance and Density based Clusters in High-Dimensional Data, Journal of Classification, Springer, DOI: 10.1007/s00357-020-09373-2, 2020.

[Thrun et al., 2020] Thrun, M. C., Pape, F., & Ultsch, A.: Interactive Machine Learning Tool for Clustering in Visual Analytics, 7th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2020), pp. 672-680, DOI 10.1109/DSAA49011.2020.00062, IEEE, Sydney, Australia, 2020.

Examples

if(interactive()){
  data('Hepta')
  Data=Hepta$Data

  V=interactiveProjectionBasedClustering(Data)

  # with prior classification
  Cls=Hepta$Cls
  V=IPBC(Data,Cls)
}