DatabionicSwarm (version 1.1.0)

DBSclustering: Databonic swarm clustering (DBS)

Description

Automated Clustering approach of the Databonic swarm with abstact U distances, which are the geodesic distances based on high-dimensional distances combined with low dimensional graph paths by using ShortestGraphPathsC.

Usage

DBSclustering(k, DataOrDistance, BestMatches, LC, StructureType = TRUE, PlotIt = FALSE,
                 method = "euclidean",...)

Arguments

k

number of clusters, how many to you see in the topographic map (3D landscape)?

DataOrDistance

Either [1:n,1:d] Matrix of Data (n cases, d dimensions) that will be used. One DataPoint per row

or symmetric Distance matrix [1:n,1:n]

BestMatches

[1:n,1:2] Matrix with positions of Bestmatches=ProjectedPoints, one matrix line per data point

LC

grid size c(Lines,Columns)

StructureType

Optional, bool; =TRUE: compact structure of clusters assumed, =FALSE: connected structure of clusters assumed. For the two options vor Clusters, see [Thrun, 2018] or Handl et al. 2006

PlotIt

Optional, bool, Plots Dendrogramm

method

Optional, one of 39 distance methods of parDist of package parallelDist, if Data matrix is chosen above

…

Further arguments passed on to the parDist function, e.g. user-defined distance functions

Value

Cls [1:n] vector with selected classes of the bestmatches. You can use plotTopographicMap(Umatrix,Bestmatches,Cls) for verification.

Details

DBS is a flexible and robust clustering framework that consists of three independent modules. The first module is the parameter-free projection method Pswarm Pswarm, which exploits the concepts of self-organization and emergence, game theory, swarm intelligence and symmetry considerations. The second module is a parameter-free high-dimensional data visualization technique, which generates projected points on a topographic map with hypsometric colors GeneratePswarmVisualization, called the generalized U-matrix. The third module is a clustering method with no sensitive parameters DBSclustering (see [Thrun, 2018, p. 104 ff]). The clustering can be verified by the visualization and vice versa. The term DBS refers to the method as a whole.

References

[Thrun, 2018] Thrun, M. C.: Projection Based Clustering through Self-Organization and Swarm Intelligence, doctoral dissertation 2017, Springer, Heidelberg, ISBN: 978-3-658-20539-3, https://doi.org/10.1007/978-3-658-20540-9, 2018.

Examples

# NOT RUN {
data("Lsun3D")
Data=Lsun3D$Data
InputDistances=as.matrix(dist(Data))
# }
# NOT RUN {
projection=Pswarm(InputDistances)
#autmatic Clustering without GeneralizedUmatrix visualization
Cls=DBSclustering(k=3, Data, 

projection$ProjectedPoints, projection$LC,PlotIt=TRUE)
# }
# NOT RUN {
visualization=GeneratePswarmVisualization(Data,

projection$ProjectedPoints,projection$LC)
## Sometimes an automatic Clustering can be improved 
## thorugh an interactive approach, 
## e.g. if Outliers exist (see [Thrun/Ultsch, 2017])
library(ProjectionBasedClustering)
Cls2=ProjectionBasedClustering::interactiveClustering(visualization$Umatrix, 
visualization$Bestmatches, Cls)
# }
# NOT RUN {
# }