| Title: | Projection Based Clustering |
|---|---|
| Description: | A clustering approach applicable to every projection method is proposed here. The two-dimensional scatter plot of any projection method can construct a topographic map which displays unapparent data structures by using distance and density information of the data. The generalized U*-matrix renders this visualization in the form of a topographic map, which can be used to automatically define the clusters of high-dimensional data. The whole system is based on Thrun and Ultsch, "Using Projection based Clustering to Find Distance and Density based Clusters in High-Dimensional Data" <DOI:10.1007/s00357-020-09373-2>. Selecting the correct projection method will result in a visualization in which mountains surround each cluster. The number of clusters can be determined by counting valleys on the topographic map. Most projection methods are wrappers for already available methods in R. By contrast, the neighbor retrieval visualizer (NeRV) is based on C++ source code of the 'dredviz' software package, and the Curvilinear Component Analysis (CCA) is translated from 'MATLAB' ('SOM Toolbox' 2.0) to R. |
| Authors: | Michael Thrun [aut, cre, cph], Florian Lerch [aut], Felix Pape [aut], Tim Schreier [aut], Luis Winckelmann [aut], Kristian Nybo [cph], Jarkko Venna [cph] |
| Maintainer: | Michael Thrun <[email protected]> |
| License: | GPL-3 |
| Version: | 1.1.9 |
| Built: | 2024-11-18 04:09:47 UTC |
| Source: | https://github.com/mthrun/projectionbasedclustering |
The package is based on a conference talk [Thrun/Ultsch, 2017], see <DOI:10.13140/RG.2.2.13124.53124>. and [Thrun/Ultsch, 2020]. The abstract of the conference talk is as follows:
Many data mining methods rely on some concept of the dissimilarity between pieces of information encoded in the data of interest. These methods can be used for cluster analysis. However, no generally accepted definition of clusters exists in the literature [Hennig et al., 2015]. Here, consistent with Bouveyron et al., it is assumed that a cluster is a group of similar objects [Bouveyron et al., 2012]. The clusters are called natural because they do not require a dissection; instead, they are clearly separated in the data [Duda et al., 2001, Theodoridis/Koutroumbas, 2009, pp. 579, 600]. These clusters can be identified by distance or density based high-dimensional structures. Dimensionality reduction techniques are able to reduce the dimensions of the input space to facilitate the exploration of structures in high-dimensional data. If they are used for visualization, they are called projection methods. The generalized U*-matrix technique is applicable for these and can be used to visualize both distance- and density-based structures [Thrun 2018; Ultsch/Thrun, 2017]. The idea that the abstract U*-matrix (AU-matrix) can be used for clustering [Ultsch et al., 2016]. The distances required for hierarchical clustering are defined by the AU-matrix [Lötsch/Ultsch, 2014]. Using this distance we propose a clustering approach for every projection method based on the U*-matrix visualization of a topographic map [Thrun 2018; Thrun/Ultsch, 2017]. The number of clusters and the cluster structure can be estimated by counting the valleys in a topographic map [Thrun et al., 2016]. If the number of clusters and the clustering method are chosen correctly, then the clusters will be well separated by mountains in the visualization. Outliers are represented as volcanoes and can be interactively marked in the visualization after the automated clustering process.
A comparison to 32 common clustering algorithms is provided in [Thrun/Ultsch, 2020].
If you want to verifiy your clustering result externally, you can use Heatmap or SilhouettePlot of the CRAN package DataVisualizations.
Additionally you can use the standard ShepardScatterPlot or the better approach through the ShepardDensityPlot of the CRAN package DataVisualizations.
Michael Thrun, Felix Pape, Florian Lerch, Tim Schreier, Luis Winckelmann
[Thrun/Ultsch, 2017] Thrun, M.C., Ultsch, A.: Projection based Clustering, Conf. Int. Federation of Classification Societies (IFCS),DOI:10.13140/RG.2.2.13124.53124, Tokyo, 2017.
[Bouveyron et al., 2012] Bouveyron, C., Hammer, B., & Villmann, T.: Recent developments in clustering algorithms, Proc. ESANN, Citeseer, 2012.
[Duda et al., 2001] Duda, R. O., Hart, P. E., & Stork, D. G.: Pattern classification, (Second Edition ed.), Ney York, USA, John Wiley & Sons, ISBN: 0-471-05669-3, 2001.
[Hennig et al., 2015] Hennig, C., Meila, M., Murtagh, F., & Rocci, R.: Handbook of cluster analysis, New York, USA, CRC Press, ISBN: 9781466551893, 2015.
[Lötsch/Ultsch, 2014] Lötsch, J., & Ultsch, A.: Exploiting the Structures of the U-Matrix, in Villmann, T., Schleif, F.-M., Kaden, M. & Lange, M. (eds.), Proc. Advances in Self-Organizing Maps and Learning Vector Quantization, pp. 249-257, Springer International Publishing, Mittweida, Germany, 2014.
[Theodoridis/Koutroumbas, 2009] Theodoridis, S., & Koutroumbas, K.: Pattern Recognition, (Fourth Edition ed.), Canada, Elsevier, ISBN: 978-1-59749-272-0, 2009.
[Thrun et al., 2016] Thrun, M. C., Lerch, F., Lötsch, 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.
[Ultsch et al., 2016] Ultsch, A., Behnisch, M., & Lötsch, J.: ESOM Visualizations for Quality Assessment in Clustering, In Merényi, E., Mendenhall, J. M. & O'Driscoll, P. (Eds.), Advances in Self-Organizing Maps and Learning Vector Quantization: Proceedings of the 11th International Workshop WSOM 2016, Houston, Texas, USA, January 6-8, 2016, (10.1007/978-3-319-28518-4_3pp. 39-48), Cham, Springer International Publishing, 2016.
[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, 2020] Thrun, M. C., & Ultsch, A.: Using Projection based Clustering to Find Distance and Density based Clusters in High-Dimensional Data, Journal of Classification, Vol. in press, Springer, DOI: 10.1007/s00357-020-09373-2, 2020.
data('Hepta') #2d projection # Visualizuation of GeneralizedUmatrix projectionpoints=NeRV(Hepta$Data) #Computation of Generalized Umatrix library(GeneralizedUmatrix) visualization=GeneralizedUmatrix(Data = Hepta$Data,projectionpoints) TopviewTopographicMap(visualization$Umatrix,visualization$Bestmatches) #or in 3D if rgl package exists #plotTopographicMap(visualization$Umatrix,visualization$Bestmatches) ##Interactive Island Generation ## from a tiled Umatrix (toroidal assumption) ## Not run: Imx = ProjectionBasedClustering::interactiveGeneralizedUmatrixIsland(visualization$Umatrix, visualization$Bestmatches) #plotTopographicMap(visualization$Umatrix,visualization$Bestmatches, Imx = Imx) ## End(Not run) # Automatic Clustering LC=c(visualization$Lines,visualization$Columns) # number of Cluster from dendrogram or visualization (PlotIt=TRUE) Cls=ProjectionBasedClustering(k=7, Hepta$Data, visualization$Bestmatches, LC,PlotIt=TRUE) # Verification library(GeneralizedUmatrix) TopviewTopographicMap(visualization$Umatrix,visualization$Bestmatches,Cls) #or in 3D if rgl package exists #plotTopographicMap(visualization$Umatrix,visualization$Bestmatches,Cls) ## Sometimes you can improve a Clustering interactivly or mark additional Outliers manually ## Not run: Cls2 = interactiveClustering(visualization$Umatrix, visualization$Bestmatches, Cls) ## End(Not run)data('Hepta') #2d projection # Visualizuation of GeneralizedUmatrix projectionpoints=NeRV(Hepta$Data) #Computation of Generalized Umatrix library(GeneralizedUmatrix) visualization=GeneralizedUmatrix(Data = Hepta$Data,projectionpoints) TopviewTopographicMap(visualization$Umatrix,visualization$Bestmatches) #or in 3D if rgl package exists #plotTopographicMap(visualization$Umatrix,visualization$Bestmatches) ##Interactive Island Generation ## from a tiled Umatrix (toroidal assumption) ## Not run: Imx = ProjectionBasedClustering::interactiveGeneralizedUmatrixIsland(visualization$Umatrix, visualization$Bestmatches) #plotTopographicMap(visualization$Umatrix,visualization$Bestmatches, Imx = Imx) ## End(Not run) # Automatic Clustering LC=c(visualization$Lines,visualization$Columns) # number of Cluster from dendrogram or visualization (PlotIt=TRUE) Cls=ProjectionBasedClustering(k=7, Hepta$Data, visualization$Bestmatches, LC,PlotIt=TRUE) # Verification library(GeneralizedUmatrix) TopviewTopographicMap(visualization$Umatrix,visualization$Bestmatches,Cls) #or in 3D if rgl package exists #plotTopographicMap(visualization$Umatrix,visualization$Bestmatches,Cls) ## Sometimes you can improve a Clustering interactivly or mark additional Outliers manually ## Not run: Cls2 = interactiveClustering(visualization$Umatrix, visualization$Bestmatches, Cls) ## End(Not run)
CCA Projects data vectors using Curvilinear Component Analysis [Demartines/Herault, 1995],[Demartines/Herault, 1997].
Unknown values (NaN's) in the data: projections of vectors with unknown components tend to drift towards the center of the projection distribution. Projections of totally unknown vectors are set to unknown (NaN).
CCA(DataOrDistances,Epochs,OutputDimension=2,method='euclidean', alpha0 = 0.5, lambda0,PlotIt=FALSE,Cls)CCA(DataOrDistances,Epochs,OutputDimension=2,method='euclidean', alpha0 = 0.5, lambda0,PlotIt=FALSE,Cls)
DataOrDistances |
Numerical matrix defined as either
or
|
Epochs |
Number of eppochs (scalar), i.e, training length |
OutputDimension |
Number of dimensions in the Outputspace, default=2 |
method |
method specified by distance string. One of: 'euclidean','cityblock=manhatten','cosine','chebychev','jaccard','minkowski','manhattan','binary' |
alpha0 |
(scalar) initial step size, 0.5 by default |
lambda0 |
(scalar) initial radius of influence, 3*max(std(D)) by default |
PlotIt |
Default: FALSE, If TRUE: Plots the projection as a 2d visualization. OutputDimension>2: only the first two dimensions will be shown |
Cls |
[1:n,1] Optional,: only relevant if PlotIt=TRUE. Numeric vector, given Classification in numbers: every element is the cluster number of a certain corresponding element of data. |
An short overview of different types of projection methods can be found in [Thrun, 2018, p.42, Fig. 4.1] (doi:10.1007/978-3-658-20540-9).
A n by OutputDimension matrix containing coordinates of the projected points.
Only Transfered from matlab to R. Matlabversion: Contributed to SOM Toolbox 2.0, February 2nd, 2000 by Juha Vesanto https://www.cis.hut.fi/projects/somtoolbox/
You can use the standard ShepardScatterPlot or the better approach through the ShepardDensityPlot of the CRAN package DataVisualizations.
Florian Lerch
[Demartines/Herault, 1997] Demartines, P., & Herault, J.: Curvilinear component analysis: A self-organizing neural network for nonlinear mapping of data sets, IEEE Transactions on Neural Networks, Vol. 8(1), pp. 148-154. 1997.
[Demartines/Herault, 1995] Demartines, P., & Herault, J.: CCA:" Curvilinear component analysis", Proc. 15 Colloque sur le traitement du signal et des images, Vol. 199, GRETSI, Groupe d'Etudes du Traitement du Signal et des Images, France 18-21 September, 1995.
data('Hepta') Data=Hepta$Data Proj=CCA(Data,Epochs=20) ## Not run: PlotProjectedPoints(Proj$ProjectedPoints,Hepta$Cls) ## End(Not run)data('Hepta') Data=Hepta$Data Proj=CCA(Data,Epochs=20) ## Not run: PlotProjectedPoints(Proj$ProjectedPoints,Hepta$Cls) ## End(Not run)
Defines the default color sequence for plots made within the Projections package.
data("DefaultColorSequence")data("DefaultColorSequence")
A vector with 562 different strings describing colors for plots.
Calculates the adjacency matrix of the delaunay graph for BestMatches (BMs) in tiled form if BestMatches are located on a toroid grid
Delaunay4Points(Points, IsToroid = TRUE,Grid=NULL,PlotIt=FALSE)Delaunay4Points(Points, IsToroid = TRUE,Grid=NULL,PlotIt=FALSE)
Points |
[1:n,1:3] matrix containing the BMKey, X and Y coordinates of the n, BestMatches NEED NOT BE UNIQUE, however, there is an edge in the Deaunay between duplicate points! |
IsToroid |
OPTIONAL, logical, indicating if BM's are on a toroid grid. Default is True |
Grid |
OPTIONAL, A vector of length 2, containing the number of lines and columns of the Grid |
PlotIt |
OPTIONAL, bool, Plots the graph |
as
Delaunay[1:n,1:n] adjacency matrix of the Delaunay-Graph
Michael Thrun
[Thrun, 2018] Thrun, M. C.: Projection Based Clustering through Self-Organization and Swarm Intelligence, doctoral dissertation 2017, Springer, ISBN: 978-3-658-20539-3, Heidelberg, 2018.
Dijkstra's SSSP (Single source shortest path) algorithm:
gets the shortest path (geodesic distance) from source vertice(point) to all other vertices(points) defined by the edges of the adjasency matrix
DijkstraSSSP(Adj, Costs, source)DijkstraSSSP(Adj, Costs, source)
Adj |
[1:n,1:n] 0/1 adjascency matrix, e.g. from delaunay graph or gabriel graph |
Costs |
[1:n,1:n] matrix, distances between n points (normally euclidean) |
source |
int, vertice(point) from which to calculate the geodesic distance to all other points |
Preallocating space for DataStructures accordingly to the maximum possible number of vertices which is fixed set at the number 10001.
ShortestPaths[1:n] vector, shortest paths (geodesic) to all other vertices including the source vertice itself
runs in O(E*Log(V))
Michael Thrun
uses a changed code which is inspired by Shreyans Sheth 28.05.2015, see https://ideone.com/qkmt31
clearly defined clusters, different variances
data("Hepta")data("Hepta")
Size 212, Dimensions 3, stored in Hepta$Data
Classes 7, stored in Hepta$Cls
[Thrun/Ultsch, 2020] Thrun, M. C., & Ultsch, A.: Clustering Benchmark Datasets Exploiting the Fundamental Clustering Problems, Data in Brief,Vol. 30(C), pp. 105501, DOI 10.1016/j.dib.2020.105501 , 2020.
data(Hepta) str(Hepta)data(Hepta) str(Hepta)
Independent Component Analysis:
Negentropie: difference of entropy to a corresponding normally-distributed random variable J(y)=|E(G(y)-E(G(v)))|^2
ICA(Data,OutputDimension=2,Contrastfunction="logcosh", Alpha=1,Iterations=200,PlotIt=FALSE,Cls)ICA(Data,OutputDimension=2,Contrastfunction="logcosh", Alpha=1,Iterations=200,PlotIt=FALSE,Cls)
Data |
numerical matrix of n cases in rows, d variables in columns, matrix is not symmetric. |
OutputDimension |
Number of dimensions in the Outputspace, default=2 |
Contrastfunction |
Maximierung der Negentropie ueber geeignete geeignete Kontrastfunktion Default: 'logcosh' G(u)=1/a*log cosh(a*u) 'exp': G(u)=-exp(u^2/2) |
Alpha |
onstant with 1<=alpha<=2 used in approximation to neg-entropy when fun == "logcosh" |
Iterations |
maximum number of iterations to perform. |
PlotIt |
Default: FALSE, If TRUE: Plots the projection as a 2d visualization. OutputDimension>2: only the first two dimensions will be shown |
Cls |
[1:n,1] Optional,: only relevant if PlotIt=TRUE. Numeric vector, given Classification in numbers: every element is the cluster number of a certain corresponding element of data. |
An short overview of different types of projection methods can be found in [Thrun, 2018, p.42, Fig. 4.1] (doi:10.1007/978-3-658-20540-9).
ProjectedPoints |
[1:n,OutputDimension], n by OutputDimension matrix containing coordinates of the Projectio |
Mixing |
[1:OutputDimension,1:d] Mischungsmatrix s.d gilt Data=MixingMatrix*ProjectedPoints |
Unmixing |
Entmischungsmatrix with Data*Unmixing=ProjectedPoints |
PCMatrix |
pre-whitening matrix that projects data onto the first n.comp principal components. |
A wrapper for fastICA
You can use the standard ShepardScatterPlot or the better approach through the ShepardDensityPlot of the CRAN package DataVisualizations.
Michael Thrun
data('Hepta') Data=Hepta$Data Proj=ICA(Data) ## Not run: PlotProjectedPoints(Proj$ProjectedPoints,Hepta$Cls) ## End(Not run)data('Hepta') Data=Hepta$Data Proj=ICA(Data) ## Not run: PlotProjectedPoints(Proj$ProjectedPoints,Hepta$Cls) ## End(Not run)
This tool is an interactive shiny tool that visualizes a given generalized Umatrix and allows the user to select areas and mark them as clusters to improve a projection based clustering.
Umatrix |
[1:Lines,1:Columns] Matrix of Umatrix Heights |
Bestmaches |
[1:n,1:2] Array with positions of Bestmatches |
Cls |
[1:n] Classification of the Bestmatches |
Clicking on "Quit" returns the Cls vector to the workspace.
List of
EnlargedUmatrix |
[1:Lines,1:Columns] Matrix of Umatrix Heights taken four times and arranged in a square 2x2. |
EnlargedBestmaches |
[1:n,1:2] Array with positions of Bestmatches according to the enlarged umatrix. |
EnlargedCls |
[1:n] Classification of the Bestmatches according to the enlarged umatrix. |
Umatrix |
[1:Lines,1:Columns] Matrix of Umatrix Heights |
Bestmaches |
[1:n,1:2] Array with positions of Bestmatches |
Cls |
[1:n] Classification of the Bestmatches |
TopView_TopographicMap |
Plot of a topographic map. |
If you want to verifiy your clustering result externally, you can use Heatmap or SilhouettePlot of the CRAN package DataVisualizations.
Florian Lerch, Michael Thrun
[Thrun/Ultsch, 2017] Thrun, M.C., Ultsch, A.: Projection based Clustering, Conf. Int. Federation of Classification Societies (IFCS),DOI:10.13140/RG.2.2.13124.53124, Tokyo, 2017.
[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, doi:10.1007/978-3-658-20540-9, 2018.
data('Hepta') #2d projection # Visualizuation of GeneralizedUmatrix projectionpoints=NeRV(Hepta$Data) #Computation of Generalized Umatrix library(GeneralizedUmatrix) visualization=GeneralizedUmatrix(Data = Hepta$Data,projectionpoints) ## Semi-Automatic Clustering done interactivly in a shiny gui ## Not run: Cls = interactiveClustering(visualization$Umatrix, visualization$Bestmatches) ##Plotting plotTopographicMap(visualization$Umatrix,visualization$Bestmatches,Cls) ## End(Not run)data('Hepta') #2d projection # Visualizuation of GeneralizedUmatrix projectionpoints=NeRV(Hepta$Data) #Computation of Generalized Umatrix library(GeneralizedUmatrix) visualization=GeneralizedUmatrix(Data = Hepta$Data,projectionpoints) ## Semi-Automatic Clustering done interactivly in a shiny gui ## Not run: Cls = interactiveClustering(visualization$Umatrix, visualization$Bestmatches) ##Plotting plotTopographicMap(visualization$Umatrix,visualization$Bestmatches,Cls) ## End(Not run)
The toroid Umatrix is usually drawn 4 times, so that connected areas on borders can be seen as a whole. An island is a manual cutout of such a tiled visualization, that is selected such that all connected areas stay intact. This shiny tool allows the user to do this manually.
interactiveGeneralizedUmatrixIsland(Umatrix, Bestmatches=NULL, Cls=NULL)interactiveGeneralizedUmatrixIsland(Umatrix, Bestmatches=NULL, Cls=NULL)
Umatrix |
[1:Lines,1:Columns] Matrix of Umatrix Heights |
Bestmatches |
[1:n, 1:2] Matrix with positions of Bestmatches for n datapoints, first columns is the position in |
Cls |
Classification of the Bestmatches |
Clicking on "Quit" returns the Imx matrix to the workspace. Details can bee read in [Thrun et al, 2016, Thrun/Ultsch, 2017].
Boolean Matrix that represents the island within the tiled Umatrix.
This function is a depricated version of a function from the Umatrix packages created by Florian Lerch and Michael Thrun
Michael Thrun
[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,Plzen, 2016.
[Thrun/Ultsch, 2017] Thrun, M.C., Ultsch, A.: Projection based Clustering, Conf. Int. Federation of Classification Societies (IFCS),<DOI:10.13140/RG.2.2.13124.53124>, Tokyo, 2017.
data("Hepta") Data=Hepta$Data Cls=Hepta$Cls InputDistances=as.matrix(dist(Data)) res=cmdscale(d=InputDistances, k = 2, eig = TRUE, add = FALSE, x.ret = FALSE) ProjectedPoints=as.matrix(res$points) #see also ProjectionBasedClustering package for other common projection methods library(GeneralizedUmatrix) resUmatrix=GeneralizedUmatrix(Data,ProjectedPoints) TopviewTopographicMap(resUmatrix$Umatrix,resUmatrix$Bestmatches,Cls) #or in 3D if rgl package exists #plotTopographicMap(resUmatrix$Umatrix,resUmatrix$Bestmatches,Cls) ##Interactive Island Generation ## from a tiled Umatrix (toroidal assumption) ## Not run: Imx = interactiveGeneralizedUmatrixIsland(resUmatrix$Umatrix, resUmatrix$Bestmatches) plotTopographicMap(resUmatrix$Umatrix, resUmatrix$Bestmatches, Imx = Imx) ## End(Not run)data("Hepta") Data=Hepta$Data Cls=Hepta$Cls InputDistances=as.matrix(dist(Data)) res=cmdscale(d=InputDistances, k = 2, eig = TRUE, add = FALSE, x.ret = FALSE) ProjectedPoints=as.matrix(res$points) #see also ProjectionBasedClustering package for other common projection methods library(GeneralizedUmatrix) resUmatrix=GeneralizedUmatrix(Data,ProjectedPoints) TopviewTopographicMap(resUmatrix$Umatrix,resUmatrix$Bestmatches,Cls) #or in 3D if rgl package exists #plotTopographicMap(resUmatrix$Umatrix,resUmatrix$Bestmatches,Cls) ##Interactive Island Generation ## from a tiled Umatrix (toroidal assumption) ## Not run: Imx = interactiveGeneralizedUmatrixIsland(resUmatrix$Umatrix, resUmatrix$Bestmatches) plotTopographicMap(resUmatrix$Umatrix, resUmatrix$Bestmatches, Imx = Imx) ## End(Not run)
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].
interactiveProjectionBasedClustering(Data, Cls=NULL) IPBC(Data, Cls=NULL)interactiveProjectionBasedClustering(Data, Cls=NULL) IPBC(Data, Cls=NULL)
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. |
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.
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 |
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 |
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.
Tim Schreier, Felix Pape, Luis Winckelmann, Michael Thrun
[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.
if(interactive()){ data('Hepta') Data=Hepta$Data V=interactiveProjectionBasedClustering(Data) # with prior classification Cls=Hepta$Cls V=IPBC(Data,Cls) }if(interactive()){ data('Hepta') Data=Hepta$Data V=interactiveProjectionBasedClustering(Data) # with prior classification Cls=Hepta$Cls V=IPBC(Data,Cls) }
Isomap procetion as introduced in 2000 by Tenenbaum, de Silva and Langford
Even with a manifold structure, the sampling must be even and dense so that dissimilarities along a manifold are shorter than across the folds. If data do not have such a manifold structure, the results are very sensitive to parameter values.
Isomap(Distances,k,OutputDimension=2,PlotIt=FALSE,Cls)Isomap(Distances,k,OutputDimension=2,PlotIt=FALSE,Cls)
Distances |
Symmetric [1:n,1:n] distance matrix, e.g. |
k |
number of k nearest neighbors, if the data is fragmented choose an higher k |
OutputDimension |
Number of dimensions in the output space, default = 2 |
PlotIt |
Default: FALSE, If TRUE: Plots the projection as a 2d visualization. If OutputDimension > 2 only the first two dimensions will be shown. |
Cls |
Optional and only relevant if PlotIt=TRUE. Numeric vector, given Classification in numbers: every element is the cluster number of a certain corresponding element of data. |
An short overview of different types of projection methods can be found in [Thrun, 2018, p.42, Fig. 4.1] (doi:10.1007/978-3-658-20540-9).
ProjectedPoints[1:n,OutputDimension] n by OutputDimension matrix containing coordinates of the Projection: A matrix of the fitted configuration..
A wrapper enabling a planar projection of the manifold learning method based on the isomap of the package vegan
if Data fragmented choose an higher k
You can use the standard ShepardScatterPlot or the better approach through the ShepardDensityPlot of the CRAN package DataVisualizations.
Michael Thrun
data('Hepta') Data=Hepta$Data Proj=Isomap(as.matrix(dist(Data)),k=7) ## Not run: PlotProjectedPoints(Proj$ProjectedPoints,Hepta$Cls) ## End(Not run)data('Hepta') Data=Hepta$Data Proj=Isomap(as.matrix(dist(Data)),k=7) ## Not run: PlotProjectedPoints(Proj$ProjectedPoints,Hepta$Cls) ## End(Not run)
Calculates the stress as defined by Kruskal for 2 distance matrices
KruskalStress(InputDistances, OutputDistances)KruskalStress(InputDistances, OutputDistances)
InputDistances |
Distance matrix of the original Data |
OutputDistances |
Distance matrix of the projected Data |
An short overview of different types of quality measures can be found in [Thrun, 2018, p.68, Fig. 6.3] (doi:10.1007/978-3-658-20540-9).
A single numerical representing the Kruskal stress of the distance matrices.
Felix Pape
Classical multidimensional scaling of a data matrix. Also known as principal coordinates analysis
MDS(DataOrDistances,method='euclidean',OutputDimension=2,PlotIt=FALSE,Cls)MDS(DataOrDistances,method='euclidean',OutputDimension=2,PlotIt=FALSE,Cls)
DataOrDistances |
Numerical matrix defined as either
or
|
method |
method specified by distance string: 'euclidean','cityblock=manhatten','cosine','chebychev','jaccard','minkowski','manhattan','binary' |
OutputDimension |
Number of dimensions in the Outputspace, default=2 |
PlotIt |
Default: FALSE, If TRUE: Plots the projection as a 2d visualization. |
Cls |
[1:n,1] Optional,: only relevant if PlotIt=TRUE. Numeric vector, given Classification in numbers: every element is the cluster number of a certain corresponding element of data. |
An short overview of different types of projection methods can be found in [Thrun, 2018, p.42, Fig. 4.1] (doi:10.1007/978-3-658-20540-9).
ProjectedPoints |
[1:n,OutputDimension], n by OutputDimension matrix containing coordinates of the Projectio |
Eigenvalues |
the eigenvalues of MDSvalues*MDSvalues' |
Stress |
Shephard-Kruskal Stress |
A wrapper for cmdscale
You can use the standard ShepardScatterPlot or the better approach through the ShepardDensityPlot of the CRAN package DataVisualizations.
Michael Thrun
data('Hepta') Data=Hepta$Data Proj=MDS(Data) ## Not run: PlotProjectedPoints(Proj$ProjectedPoints,Hepta$Cls) ## End(Not run)data('Hepta') Data=Hepta$Data Proj=MDS(Data) ## Not run: PlotProjectedPoints(Proj$ProjectedPoints,Hepta$Cls) ## End(Not run)
Projection is done by the neighbor retrieval visualizer (NeRV)
NeRV(Data, lambda = 0.1, neighbors = 20, iterations = 10, cg_steps = 2, cg_steps_final = 40, randominit = T, OutputDimension = 2, PlotIt = FALSE, Cls)NeRV(Data, lambda = 0.1, neighbors = 20, iterations = 10, cg_steps = 2, cg_steps_final = 40, randominit = T, OutputDimension = 2, PlotIt = FALSE, Cls)
Data |
Numerical matrix of the Data to be projected, [1:n,1:d], nonsymmetric, and consists of n cases of d-dimensional data points with every case having d attributes, variables or features |
lambda |
Optional: Controls the trustworthiness-continuity tradeoff. Default = 0.1 |
neighbors |
Optional: Set the number of nearest neighbours that each point should have. Should be positive. Default = 20 |
iterations |
Optional: The number of iterations to perform. Default = 10 |
cg_steps |
Optional: The number of conjugate gradient steps to perform per iteration in NeRV's optimization scheme. Default = 2 |
cg_steps_final |
Optional: The number of conjugate gradient steps to perform on the final iteration in NeRV's optimization scheme. Default = 40 |
randominit |
Optional: TRUE: Random Initialization (default), FALSE: PCA initializiation |
OutputDimension |
Optional: Number of dimensions in the Outputspace, default=2 |
PlotIt |
Optional: Should the projected points be plotted? Default: FALSE. Note: this is only usefull if OutputDimension = 2. |
Cls |
Optional: Vector containing the number of the class for each row in Data. This is only used to color the points according to their classes if PlotIt = T |
Uses the NeRV projection with matrix Data and lambda. Lambda controls the trustworthiness-continuity tradeoff.
An short overview of different types of projection methods can be found in [Thrun, 2018, p.42, Fig. 4.1] (doi:10.1007/978-3-658-20540-9).
OutputDimension-dimensional matrix of projected points
PCA initialization changes form the original C++ Sourcecode of https://research.cs.aalto.fi/pml/software/dredviz/ to the R version of the projections package. Other changes are made only regarding data types of Rcpp in comparison to the original C++ Source code.
You can use the standard ShepardScatterPlot or the better approach through the ShepardDensityPlot of the CRAN package DataVisualizations.
Michael Thrun, Felix Pape
Jarkko Venna, Jaakko Peltonen, Kristian Nybo, Helena Aidos, and Samuel Kaski. Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization. Journal of Machine Learning Research, 11:451-490, 2010.
Jarkko Venna and Samuel Kaski. Nonlinear Dimensionality Reduction as Information Retrieval. In Marina Meila and Xiaotong Shen, editors, Proceedings of AISTATS 2007, the 11th International Conference on Artificial Intelligence and Statistics. Omnipress, 2007. JMLR Workshop and Conference Proceedings, Volume 2: AISTATS 2007.
data('Hepta') Data=Hepta$Data ## Not run: Proj=NeRV(Data) PlotProjectedPoints(Proj$ProjectedPoints,Hepta$Cls) ## End(Not run)data('Hepta') Data=Hepta$Data ## Not run: Proj=NeRV(Data) PlotProjectedPoints(Proj$ProjectedPoints,Hepta$Cls) ## End(Not run)
Performs a principal components analysis on the given data matrix projection=SammonsMapping(Data)
PCA(Data,OutputDimension=2,Scale=FALSE,Center=FALSE,PlotIt=FALSE,Cls)PCA(Data,OutputDimension=2,Scale=FALSE,Center=FALSE,PlotIt=FALSE,Cls)
Data |
numerical matrix of data: n cases in rows, d variables in columns |
OutputDimension |
Number of dimensions in the Outputspace, default=2 |
Scale |
a logical value indicating whether the variables should be scaled to have unit variance before the analysis takes place. The default is FALSE for consistency with S, but in general scaling is advisable. Alternatively, a vector of length equal the number of columns of x can be supplied. The value is passed to scale. |
Center |
a logical value indicating whether the variables should be shifted to be zero centered. Alternately, a vector of length equal the number of columns of x can be supplied. The value is passed to scale |
PlotIt |
Default: FALSE, If TRUE: Plots the projection as a 2d visualization. OutputDimension>2: only the first two dimensions will be shown |
Cls |
[1:n,1] Optional,: only relevant if PlotIt=TRUE. Numeric vector, given Classification in numbers: every element is the cluster number of a certain corresponding element of data. |
An short overview of different types of projection methods can be found in [Thrun, 2018, p.42, Fig. 4.1] (doi:10.1007/978-3-658-20540-9).
ProjectedPoints |
[1:n,OutputDimension], n by OutputDimension matrix containing coordinates of the Projectio |
Rotation |
the matrix of variable loadings (i.e., a matrix whose columns contain the eigenvectors) |
sDev |
the standard deviations of the principal components (i.e., the square roots of the eigenvalues of the covariance/correlation matrix, though the calculation is actually done with the singular values of the data matrix) |
TransformedData |
matrix with PCA transformed Data |
Center |
the centering used, or FALSE |
Scale |
the scaling used, or FALSE |
A wrapper for prcomp
You can use the standard ShepardScatterPlot or the better approach through the ShepardDensityPlot of the CRAN package DataVisualizations.
Michael Thrun
data('Hepta') Data=Hepta$Data Proj=PCA(Data) ## Not run: PlotProjectedPoints(Proj$ProjectedPoints,Hepta$Cls) ## End(Not run)data('Hepta') Data=Hepta$Data Proj=PCA(Data) ## Not run: PlotProjectedPoints(Proj$ProjectedPoints,Hepta$Cls) ## End(Not run)
plots XY data colored by Cls with ggplot2
PlotProjectedPoints(Points,Cls,BMUorProjected=F,PlotLegend=FALSE, xlab='X',ylab='Y',main="Projected Points",PointSize=2.5)PlotProjectedPoints(Points,Cls,BMUorProjected=F,PlotLegend=FALSE, xlab='X',ylab='Y',main="Projected Points",PointSize=2.5)
Points |
[1:n,1:2] xy cartesian coordinates of a projection |
Cls |
numeric vector, given Classification in numbers: every element is the cluster number of a certain corresponding element of data. |
BMUorProjected |
Default ==FALSE, If TRUE assuming BestMatches of ESOM instead of Projected Points |
PlotLegend |
... |
xlab |
Optional: Label of the x axis |
ylab |
Optional: Label of the y axis |
main |
Optional: title |
PointSize |
Optional: size of points |
ggobject of ggplot2
Michael Thrun
Swarm-based Projection method using game theory published in [Thrun/Ultsch, 2020].
PolarSwarm(DataOrDistances, method = "euclidean", PlotIt = FALSE, Cls)PolarSwarm(DataOrDistances, method = "euclidean", PlotIt = FALSE, Cls)
DataOrDistances |
Numerical matrix defined as either
or
|
method |
If |
PlotIt |
Default: FALSE, If TRUE: Plots the projection as a 2d visualization. OutputDimension>2: only the first two dimensions will be shown |
Cls |
Optional,: only relevant if PlotIt=TRUE. Numeric vector, given Classification in numbers: every element is the cluster number of a certain corresponding element of data. |
By exploting swarm intelligence and game theory no parameter have to be set.
List of
ProjectedPoints |
[1:n,2], n by 2 matrix containing coordinates of the Projection |
ModelObject |
output of |
Michael Thrun
[Thrun/Ultsch, 2020] Thrun, M. C., & Ultsch, A.: Swarm Intelligence for Self-Organized Clustering, Artificial intelligence, Vol. in press, pp. doi 10.1016/j.artint.2020.103237, 2020.
data('Hepta') Data=Hepta$Data Distances=as.matrix(dist(Data)) Proj=PolarSwarm(Data) ## Not run: PlotProjectedPoints(Proj$ProjectedPoints,Hepta$Cls) ## End(Not run)data('Hepta') Data=Hepta$Data Distances=as.matrix(dist(Data)) Proj=PolarSwarm(Data) ## Not run: PlotProjectedPoints(Proj$ProjectedPoints,Hepta$Cls) ## End(Not run)
Transformation of projected points to bestmatches defined by generalized Umatrix
Projection2Bestmatches(ProjectedPoints)Projection2Bestmatches(ProjectedPoints)
ProjectedPoints |
[1:n,1:2] n projected points in two-dimensional space. |
It is assumed that an unambiguous assignment between projected points and data points is given.
Bestmatches |
[1:n,1:2] Positions of GridConverted Projected Points, which can be used for the generalized Umatrix, to the predefined Grid by Lines and Columns. First Columns has the content of the Line No and second Column of the Column number. |
LC |
[1:2] vector if Line No. and ColumnNo. which defines the size of the grid of the generalized Umatrix |
Details of the equations used are written down in [Thrun, 2018, p. 47].
Michael Thrun
[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, doi:10.1007/978-3-658-20540-9, 2018.
data('Hepta') ProjList=MDS(Hepta$Data) trafo=Projection2Bestmatches(ProjList$ProjectedPoints)data('Hepta') ProjList=MDS(Hepta$Data) trafo=Projection2Bestmatches(ProjList$ProjectedPoints)
Three steps are necessary for PBC. First, a projection method has to be chosen to generate projected points of high-dimensional data points. Second, the generalized U*-matrix has to be applied to the projected points by using a simplified emergent self-organizing map (ESOM) algorithm which is an unsupervised neural network [Thrun, 2018]. The resulting generalized U-matrix can be visualized by the topographic map [Thrun et al., 2016]. Third, the clustering itself is built on top of the generalized U-matrix using the concept of the abstract U-Matrix and shortest graph paths using ShortestGraphPathsC.
ProjectionBasedClustering(k, DataOrDistances, BestMatches, LC, StructureType = TRUE, PlotIt = FALSE, method = "euclidean")ProjectionBasedClustering(k, DataOrDistances, BestMatches, LC, StructureType = TRUE, PlotIt = FALSE, method = "euclidean")
k |
number of clusters, how many to you see in the 3d landscape? |
DataOrDistances |
Numerical matrix that will be used for clustering with one DataPoint per row, defined as either as
or
|
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, 2017] or Handl et al. 2006 |
PlotIt |
Optional, bool, Plots Dendrogramm |
method |
Optional, distance method used in parallelDist if |
ProjectionBasedClustering is a flexible and robust clustering framework based on a chose projection method and projection method a parameter-free high-dimensional data visualization technique. The visualization combines projected points with a topographic map with hypsometric colors, defined by the generalized U-matrix (see package GeneralizedUmatrix function GeneralizedUmatrix).
The clustering method with no sensitive parameters is done in this function and the algorithm is introduced in detail in [Thrun/Ultsch, 2020]. The clustering can be verified by the visualization and vice versa. If you want to verifiy your clustering result externally, you can use Heatmap or SilhouettePlot of the CRAN package DataVisualizations.
If parallelDist is not installed, function automatically falls back to dist.
Cls [1:n] vector with selected classes of the bestmatches. You can use plotTopographicMap(Umatrix,Bestmatches,Cls) for verification.
Often it is better to mark the outliers manually after the prozess of clustering; use in this case the visualization plotTopographicMap of the package GeneralizedUmatrix. If you would like to mark the outliers interactivly in the visualization use
the interactiveClustering function.
Michael Thrun
[Thrun et al., 2016] Thrun, M. C., Lerch, F., Lötsch, 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.
[Thrun/Ultsch, 2017] Thrun, M.C., Ultsch, A.: Projection based Clustering, Conf. Int. Federation of Classification Societies (IFCS),DOI:10.13140/RG.2.2.13124.53124, Tokyo, 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, Vol. in press, Springer, DOI: 10.1007/s00357-020-09373-2, 2020.
data('Hepta') #Step I: 2d projection projectionpoints=NeRV(Hepta$Data) #Step II (Optional): Computation of Generalized Umatrix library(GeneralizedUmatrix) visualization=GeneralizedUmatrix(Data = Hepta$Data,projectionpoints) # Visualizuation of GeneralizedUmatrix library(GeneralizedUmatrix) TopviewTopographicMap(visualization$Umatrix,visualization$Bestmatches) #or in 3D if rgl package exists #plotTopographicMap(visualization$Umatrix,visualization$Bestmatches) # Step III: Automatic Clustering trafo=Projection2Bestmatches(projectionpoints) # number of Cluster from dendrogram (PlotIt=T) or visualization above Cls=ProjectionBasedClustering(k=7, Hepta$Data, trafo$Bestmatches, trafo$LC,PlotIt=TRUE) # Verification of Clustering TopviewTopographicMap(visualization$Umatrix,visualization$Bestmatches,Cls) #or in 3D if rgl package exists #plotTopographicMap(visualization$Umatrix,visualization$Bestmatches,Cls)data('Hepta') #Step I: 2d projection projectionpoints=NeRV(Hepta$Data) #Step II (Optional): Computation of Generalized Umatrix library(GeneralizedUmatrix) visualization=GeneralizedUmatrix(Data = Hepta$Data,projectionpoints) # Visualizuation of GeneralizedUmatrix library(GeneralizedUmatrix) TopviewTopographicMap(visualization$Umatrix,visualization$Bestmatches) #or in 3D if rgl package exists #plotTopographicMap(visualization$Umatrix,visualization$Bestmatches) # Step III: Automatic Clustering trafo=Projection2Bestmatches(projectionpoints) # number of Cluster from dendrogram (PlotIt=T) or visualization above Cls=ProjectionBasedClustering(k=7, Hepta$Data, trafo$Bestmatches, trafo$LC,PlotIt=TRUE) # Verification of Clustering TopviewTopographicMap(visualization$Umatrix,visualization$Bestmatches,Cls) #or in 3D if rgl package exists #plotTopographicMap(visualization$Umatrix,visualization$Bestmatches,Cls)
In the absence of a generative model for the data the algorithm can be used to find the projection pursuit directions. Projection pursuit is a technique for finding 'interesting' directions in multidimensional datasets
ProjectionPursuit(Data,OutputDimension=2,Indexfunction="logcosh", Alpha=1,Iterations=200,PlotIt=FALSE,Cls)ProjectionPursuit(Data,OutputDimension=2,Indexfunction="logcosh", Alpha=1,Iterations=200,PlotIt=FALSE,Cls)
Data |
array of data: n cases in rows, d variables in columns, matrix is not symmetric or distance matrix, in this case matrix has to be symmetric |
OutputDimension |
Number of dimensions in the Outputspace, default=2 |
Indexfunction |
Criterium for Minimization: default: 'logcosh' G(u)=1/a*log cosh(a*u) (ICA) 'exp': G(u)=-exp(u^2/2) 'kernel' 1/(1* pi )*exp(r/2) |
Alpha |
constant with 1<=alpha<=2 used in approximation to neg-entropy when fun == "logcosh" |
Iterations |
maximum number of iterations to perform. |
PlotIt |
Default: FALSE, If TRUE: Plots the projection as a 2d visualization. OutputDimension>2: only the first two dimensions will be shown |
Cls |
[1:n,1] Optional,: only relevant if PlotIt=TRUE. Numeric vector, given Classification in numbers: every element is the cluster number of a certain corresponding element of data. |
An short overview of different types of projection methods can be found in [Thrun, 2018, p.42, Fig. 4.1] (doi:10.1007/978-3-658-20540-9).
ProjectedPoints |
[1:n,OutputDimension], n by OutputDimension matrix containing coordinates of the Projectio |
You can use the standard ShepardScatterPlot or the better approach through the ShepardDensityPlot of the CRAN package DataVisualizations.
Michael Thrun
Improved MDS thorugh a normalization of the Input space
SammonsMapping(DataOrDistances,method='euclidean',OutputDimension=2,PlotIt=FALSE,Cls)SammonsMapping(DataOrDistances,method='euclidean',OutputDimension=2,PlotIt=FALSE,Cls)
DataOrDistances |
Numerical matrix defined as either
or
|
method |
method specified by distance string: 'euclidean','cityblock=manhatten','cosine','chebychev','jaccard','minkowski','manhattan','binary' |
OutputDimension |
Number of dimensions in the Outputspace, default=2 |
PlotIt |
Default: FALSE, If TRUE: Plots the projection as a 2d visualization. |
Cls |
[1:n,1] Optional,: only relevant if PlotIt=TRUE. Numeric vector, given Classification in numbers: every element is the cluster number of a certain corresponding element of data. |
An short overview of different types of projection methods can be found in [Thrun, 2018, p.42, Fig. 4.1] (doi:10.1007/978-3-658-20540-9).
ProjectedPoints |
[1:n,OutputDimension], n by OutputDimension matrix containing coordinates of the Projectio |
Stress |
Shephard-Kruskal Stress |
A wrapper for sammon
You can use the standard ShepardScatterPlot or the better approach through the ShepardDensityPlot of the CRAN package DataVisualizations.
Michael Thrun
data('Hepta') Data=Hepta$Data Proj=SammonsMapping(Data) ## Not run: PlotProjectedPoints(Proj$ProjectedPoints,Hepta$Cls) ## End(Not run)data('Hepta') Data=Hepta$Data Proj=SammonsMapping(Data) ## Not run: PlotProjectedPoints(Proj$ProjectedPoints,Hepta$Cls) ## End(Not run)
Dijkstra's SSSP (Single source shortest path) algorithm, from all points to all points
ShortestGraphPathsC(Adj, Cost)ShortestGraphPathsC(Adj, Cost)
Adj |
[1:n,1:n] 0/1 adjascency matrix, e.g. from delaunay graph or gabriel graph |
Cost |
[1:n,1:n] matrix, distances between n points (normally euclidean) |
Vertices are the points, edges have the costs defined by weights (normally a distance). The algorithm runs in runs in O(n*E*Log(V)), see also [Jungnickel, 2013, p. 87]. Further details can be foubd in [Jungnickel, 2013, p. 83-87].
ShortestPaths[1:n,1:n] vector, shortest paths (geodesic) to all other vertices including the source vertice itself from al vertices to all vertices, stored as a matrix
require C++11 standard (set flag in Compiler, if not set automatically)
Michael Thrun
[Dijkstra, 1959] Dijkstra, E. W.: A note on two problems in connexion with graphs, Numerische mathematik, Vol. 1(1), pp. 269-271. 1959.
[Jungnickel, 2013] Jungnickel, D.: Graphs, networks and algorithms, (4th ed ed. Vol. 5), Berlin, Heidelberg, Germany, Springer, ISBN: 978-3-642-32278-5, 2013.
[Thrun/Ultsch, 2017] Thrun, M.C., Ultsch, A.: Projection based Clustering, Conf. Int. Federation of Classification Societies (IFCS),DOI:10.13140/RG.2.2.13124.53124, Tokyo, 2017.
T-distributed Stochastic Neighbor Embedding res = tSNE(Data, KNN=30,OutputDimension=2)
tSNE(DataOrDistances,k,OutputDimension=2,Algorithm='tsne_cpp', method="euclidean",Whitening=FALSE, Iterations=1000,PlotIt=FALSE,Cls,...)tSNE(DataOrDistances,k,OutputDimension=2,Algorithm='tsne_cpp', method="euclidean",Whitening=FALSE, Iterations=1000,PlotIt=FALSE,Cls,...)
DataOrDistances |
Numerical matrix defined as either
or
|
k |
number of k nearest neighbors=number of effective nearest neighbors("perplexity"); Important parameter. If not given, settings of packages of t-SNE will be used depending |
OutputDimension |
Number of dimensions in the Outputspace, default=2 |
Algorithm |
tsne_cpp': T-Distributed Stochastic Neighbor Embedding using a Barnes-HutImplementation in C++ of Rtsne 'tsne_r': pure R implementation of the t-SNE algorithm of of tsne |
method |
method specified by distance string: 'euclidean','cityblock=manhatten','cosine','chebychev','jaccard','minkowski','manhattan','binary' |
Whitening |
A boolean value indicating whether the matrix data should be whitened (tsne_r) or if pca should be used priorly (tsne_cpp) |
Iterations |
maximum number of iterations to perform. |
PlotIt |
Default: FALSE, If TRUE: Plots the projection as a 2d visualization. OutputDimension>2: only the first two dimensions will be shown |
Cls |
[1:n,1] Optional,: only relevant if PlotIt=TRUE. Numeric vector, given Classification in numbers: every element is the cluster number of a certain corresponding element of data. |
... |
Further arguments passed on to either 'Rtsne' or 'tsne' |
An short overview of different types of projection methods can be found in [Thrun, 2018, p.42, Fig. 4.1] (doi:10.1007/978-3-658-20540-9).
List of
ProjectedPoints |
[1:n,OutputDimension], n by OutputDimension matrix containing coordinates of the Projection |
ModelObject |
NULL for tsne_r, further information if tsne_cpp is selected |
A wrapper for Rtsne
or for tsne
You can use the standard ShepardScatterPlot or the better approach through the ShepardDensityPlot of the CRAN package DataVisualizations.
Michael Thrun
data('Hepta') Data=Hepta$Data Proj=tSNE(Data,k=7) ## Not run: PlotProjectedPoints(Proj$ProjectedPoints,Hepta$Cls) ## End(Not run)data('Hepta') Data=Hepta$Data Proj=tSNE(Data,k=7) ## Not run: PlotProjectedPoints(Proj$ProjectedPoints,Hepta$Cls) ## End(Not run)
Uniform manifold approximation and projection is a technique for dimension reduction. The algorithm was described by [McInnes et al., 2018].
UniformManifoldApproximationProjection(DataOrDistances, k, Epochs,OutputDimension=2,Algorithm='umap_pkg',PlotIt=FALSE,Cls,...)UniformManifoldApproximationProjection(DataOrDistances, k, Epochs,OutputDimension=2,Algorithm='umap_pkg',PlotIt=FALSE,Cls,...)
DataOrDistances |
Numerical matrix defined as either
or
|
k |
number of k nearest neighbors, Important parameter, if not given, settings of package umap will be used, default of package umap is currently 15 |
Epochs |
Number of eppochs (scalar), i.e, training length, default of package umap is currently 200 |
OutputDimension |
Number of dimensions in the Outputspace, default=2 |
Algorithm |
|
PlotIt |
Default: FALSE, If TRUE: Plots the projection as a 2d visualization. OutputDimension>2: only the first two dimensions will be shown |
Cls |
Optional,: only relevant if PlotIt=TRUE. Numeric vector, given Classification in numbers: every element is the cluster number of a certain corresponding element of data. |
... |
one of the other 21 parameters that can be specified, please see |
To the knowledge of the author of this function no peer-reviewed publication of the method exists. Use with greate care.
List of
ProjectedPoints |
[1:n,OutputDimension], n by OutputDimension matrix containing coordinates of the Projection |
ModelObject |
output of |
Setting |
specific settings used in |
Uniform Manifold Approximation and Projection and U-matrix [Ultsch/Siemon, 1990] are both sometimes abbreviated with Umap. Hence the abbreveviation is omitted here.
Michael Thrun
[McInnes et al., 2018] McInnes, L., Healy, J., & Melville, J.: Umap: Uniform manifold approximation and projection for dimension reduction, arXiv preprint arXiv:1802.03426, 2018.
[Ultsch/Siemon, 1990] Ultsch, A., & Siemon, H. P.: Kohonen's Self Organizing Feature Maps for Exploratory Data Analysis, International Neural Network Conference, pp. 305-308, Kluwer Academic Press, Paris, France, 1990.
umap of umap
umap of uwot
data('Hepta') Data=Hepta$Data Proj=UniformManifoldApproximationProjection(Data) ## Not run: PlotProjectedPoints(Proj$ProjectedPoints,Hepta$Cls) ## End(Not run)data('Hepta') Data=Hepta$Data Proj=UniformManifoldApproximationProjection(Data) ## Not run: PlotProjectedPoints(Proj$ProjectedPoints,Hepta$Cls) ## End(Not run)