DatabionicSwarm (version 2.0.0)

DatabionicSwarm-package: tools:::Rd_package_title("DatabionicSwarm")

Description

tools:::Rd_package_description("DatabionicSwarm")

Author

Michal Thrun

Maintainer: tools:::Rd_package_maintainer("DatabionicSwarm")

Details

For a brief introduction to DatabionicSwarm please see the vignette Short Intro to the Databionic Swarm (DBS). The license is CC BY-NC-SA 4.0.

tools:::Rd_package_indices("DatabionicSwarm")

References

[Thrun/Ultsch, 2021] Thrun, M. C., and Ultsch, A.: Swarm Intelligence for Self-Organized Clustering, Artificial Intelligence, Vol. 290, pp. 103237, tools:::Rd_expr_doi("10.1016/j.artint.2020.103237"), 2021.

[Thrun/Ultsch, 2021] Thrun, M. C., & Ultsch, A.: Swarm Intelligence for Self-Organized Clustering (Extended Abstract), in Bessiere, C. (Ed.), 29th International Joint Conference on Artificial Intelligence (IJCAI), Vol. IJCAI-20, pp. 5125--5129, tools:::Rd_expr_doi("10.24963/ijcai.2020/720"), Yokohama, Japan, Jan., 2021.

[Thrun/Ultsch, 2020] Thrun, M. C., & Ultsch, A.: Uncovering High-Dimensional Structures of Projections from Dimensionality Reduction Methods, MethodsX, Vol. 7, pp. 101093, DOI tools:::Rd_expr_doi("10.1016/j.mex.2020.101093"), 2020.

[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, tools:::Rd_expr_doi("10.1007/978-3-658-20540-9"), 2018.

[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 et al., 2016] Thrun, M. C., Lerch, F., Loetsch, J., & Ultsch, A.: Visualization and 3D Printing of Multivariate Data of Biomarkers, in Skala, V. (Ed.), International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG), Vol. 24, Plzen, http://wscg.zcu.cz/wscg2016/short/A43-full.pdf, 2016.

Successfully used in

[Thrun et al., 2018] Thrun, M. C., Breuer, L., & Ultsch, A. : Knowledge discovery from low-frequency stream nitrate concentrations: hydrology and biology contributions, Proc. European Conference on Data Analysis (ECDA), pp. 46-47, Paderborn, Germany, 2018.

[Weyer-Menkhoff et al., 2018] Weyer-Menkhoff, I., Thrun, M. C., & Loetsch, J.: Machine-learned analysis of quantitative sensory testing responses to noxious cold stimulation in healthy subjects, European Journal of Pain, Vol. 22(5), pp. 862-874, DOI tools:::Rd_expr_doi("10.1002/ejp.1173"), 2018.

[Kringel et al., 2018] Kringel, D., Geisslinger, G., Resch, E., Oertel, B. G., Thrun, M. C., Heinemann, S., & Loetsch, J. : Machine-learned analysis of the association of next-generation sequencing based human TRPV1 and TRPA1 genotypes with the sensitivity to heat stimuli and topically applied capsaicin, Pain, Vol. 159 (7 ), pp. 1366-1381, DOI tools:::Rd_expr_doi("10.1097/j.pain.0000000000001222"), 2018

[Thrun, 2019] Thrun, M. C.: : Cluster Analysis of Per Capita Gross Domestic Products, Entrepreneurial Business and Economics Review (EBER), Vol. 7(1), pp. 217-231, DOI: tools:::Rd_expr_doi("10.15678/EBER.2019.070113"), 2019.

[Lopez-Garcia et al., 2020] Lopez-Garcia, P., Argote, D. L., & Thrun, M. C.: Projection-based Classification of Chemical Groups and Provenance Analysis of Archaeological Materials, IEEE Access, Vol. 8, pp. 152439-152451, DOI tools:::Rd_expr_doi("10.1109/ACCESS.2020.3016244"), 2020.

Examples

data('Lsun3D')
##2d projection, without instant visualization of steps

#Alternative I:
#DistanceMatrix hast to be defined by the user.
InputDistances=as.matrix(dist(Lsun3D$Data))
# \donttest{
projection=Pswarm(InputDistances)
#2d projection, with instant visualization 

if (FALSE) {
#Alternative II: DataMatrix, Distance is Euclidean per default
projection=Pswarm(Lsun3D$Data,Cls=Lsun3D$Cls,PlotIt=T)
}
#
##Computation of Generalized Umatrix
# If Non Euclidean Distances are used, Please Use \code{MDS}
# from the ProjectionBasedClustering package with the correct OutputDimension
# to generate a new DataMatrix from the distances (see SheppardDiagram
# or KruskalStress)
genUmatrixList=GeneratePswarmVisualization(Data = Lsun3D$Data,

projection$ProjectedPoints,projection$LC)
## Visualizuation of GenerelizedUmatrix, 
# Estimation of the Number of Clusters=Number of valleys
library(GeneralizedUmatrix)#install if not installed
GeneralizedUmatrix::plotTopographicMap(genUmatrixList$Umatrix,genUmatrixList$Bestmatches)
## Automatic Clustering
# number of Cluster from dendrogram (PlotIt=TRUE) or visualization 
Cls=DBSclustering(k=3, Lsun3D$Data, genUmatrixList$Bestmatches,
genUmatrixList$LC,PlotIt=FALSE)
# Verification, often its better to mark Outliers manually

GeneralizedUmatrix::plotTopographicMap(genUmatrixList$Umatrix,genUmatrixList$Bestmatches,Cls)
# }
if (FALSE) {
# To generate the 3D landscape in the shape of an island 
# from the toroidal topograpic map visualization
# you may cut your island interactivly around high mountain ranges
Imx = ProjectionBasedClustering::interactiveGeneralizedUmatrixIsland(genUmatrixList$Umatrix,
genUmatrixList$Bestmatches,Cls)

GeneralizedUmatrix::plotTopographicMap(genUmatrixList$Umatrix,
genUmatrixList$Bestmatches, Cls=Cls,Imx = Imx)
}
if (FALSE) {
library(ProjectionBasedClustering)#install if not installed
Cls2=ProjectionBasedClustering::interactiveClustering(genUmatrixList$Umatrix, 
genUmatrixList$Bestmatches, Cls)
}
# \dontshow{
data2=matrix(runif(n = 100),10,10)
distance=as.matrix(dist(data2))
res=Pswarm(distance, LC = c(10,12))
Cls=DBSclustering(k=2, data2, res$ProjectedPoints, res$LC,PlotIt=FALSE)

resUmatrix=GeneratePswarmVisualization(data2,res$ProjectedPoints,res$LC)
# }