[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$Data))projection=Pswarm(InputDistances)#2d projection, with instant visualization ## Not run:#Alternative II: DataMatrix, Distance is Euclidean per defaultprojection=Pswarm(Lsun3D$Data,Cls=Lsun3D$Cls,PlotIt=T)## End(Not run)###Computation of Generalized Umatrix# If Non Euclidean Distances are used, Please Use \code{MDS}# from the ProjectionBasedClustering package with the correct OutputDimension# to generate a new DataMatrix from the distances (see SheppardDiagram# or KruskalStress)genUmatrixList=GeneratePswarmVisualization(Data = Lsun3D$Data,projection$ProjectedPoints,projection$LC)## Visualizuation of GenerelizedUmatrix, # Estimation of the Number of Clusters=Number of valleyslibrary(GeneralizedUmatrix)#install if not installedGeneralizedUmatrix::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$LC,PlotIt=FALSE)# Verification, often its better to mark Outliers manuallyGeneralizedUmatrix::plotTopographicMap(genUmatrixList$Umatrix,genUmatrixList$Bestmatches,Cls)## Not run:# To generate the 3D landscape in the shape of an island # from the toroidal topograpic map visualization# you may cut your island interactivly around high mountain rangesImx = ProjectionBasedClustering::interactiveGeneralizedUmatrixIsland(genUmatrixList$Umatrix,genUmatrixList$Bestmatches,Cls)GeneralizedUmatrix::plotTopographicMap(genUmatrixList$Umatrix,genUmatrixList$Bestmatches, Cls=Cls,Imx = Imx)## End(Not run)## Not run:library(ProjectionBasedClustering)#install if not installedCls2=ProjectionBasedClustering::interactiveClustering(genUmatrixList$Umatrix,genUmatrixList$Bestmatches, Cls)## End(Not run)
Note
For interactive Island Generation of a generalized Umatrix see interactiveGeneralizedUmatrixIsland function in the package ProjectionBasedClustering.
If you want to verifiy your clustering result externally, you can use Heatmap or SilhouettePlot of the CRAN package DataVisualizations.