Package RidgeClust. January 18, 2011
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1 Package RidgeClust January 18, 2011 Type Package Title A clustering algorithm based on the package Mclust and a merging approach. See?ridgeline.clust Version 1.0 Date Author Grigory Alexandrovich Maintainer <alexandrovich@mathematik.uni-marburg.de> Description A 2-step clustering-algorithm. At the 1. step a Mclust- (a Gaussian-Mixture-) clustering of the data is generated. At the 2. step components which lead to unimodal or weakly bimodal mixtures are merged. The measure of the bimodality is the ridgeline ratio. See?ridgeline.clust. License GPL-2 LazyLoad yes Depends mclust, ellipse R topics documented: plot.ridgeclust RidgeClustPackage ridgeline.clust Index 6 plot.ridgeclust Plot function. Description Redefined generic plot function for the class RidgeClust. Plots results of the clustering algorithm ridgeline.clust in the case of two-dimensional data. Shows the scatterplot of the original data, the result of Mclust and the result after the merging. 1
2 2 RidgeClustPackage Usage plot(x, daten) Arguments x daten Result of the function ridgeline.clust(). Data which was clustered (a matrix or a dataframe with two columns). Author(s) G. Alexandrovich See Also ridgeline.clust Examples #See example of ridgline.clust RidgeClustPackage A clustering algorithm based on Mclust and a merging approach. See?ridgeline.clust Description Details A 2-step clustering-algorithm. At the 1. step a Mclust- (a Gaussian-Mixture-) clustering of the data is generated. At the 2. step components which lead to unimodal or weakly bimodal mixtures are merged. The measure of the bimodality is the ridgeline ratio. See?ridgeline.clust. Package: ridgeline.clust Type: Package Version: 1.0 Date: License: GPL-2 LazyLoad: yes ridgeline.clust(data) Author(s) Grigory Alexandrovich, alexandrovich@mathematik.uni-marburg.de References Alexandrovich G. (2010): Analytische Eigenschaften von Mischungen elliptischer Verteilungen und deren Anwendung in der Clusteranalyse, diploma thesis, Marburg University
3 ridgeline.clust 3 Fraley, C., Raftery, A.E. (2006): Technical Report MCLUST Version 3 for R. Hennig, C. (2009): Methods for merging Gaussian mixture components, Research Report no. 302, Department of Statistical Science, UCL Surajit Ray and Bruce G. Lindsay (2005): The topography of multivariate normal mixtures, Ann. Statist. 33, Number 5, pp See Also ridgeline.clust plot.ridgeclust ridgeline.clust Computes a clustering of the data Description A 2-step clustering-algorithm. At the 1. step a clustering based on a Gaussian mixture model is calculated for the data (the package Mclust is applied here). At the 2. step Gaussian components which lead to weakly seperated (i.e. unimodal or weakly bimodal) submixtures are merged, so that the number of clusters is reduced. Usage ridgeline.clust(daten, rst = 0.7, anz = 15, G = NULL, modelnames = NULL, prior = NULL, control = emcontrol(), initialization = NULL, warn = FALSE,...) Arguments daten rst anz G modelnames prior control Data to be clustered, a dataframe or a matrix. Cutoff value for the ridgeline ratio (0 <= rst <= 1). rst = 1 implies merging only at a strict unimodality, rst near 0 implicates merging of strongly separated components. Parameter for the mode-searching routine. For high-dimensional data must be increased. A suitable choice is (15/2) * Dimension Mclust parameter. Vector with numbers of components for the clustering. See Mclust reference Covariance structure of the components. See Mclust reference Priors. See Mclust reference Parameter-Object for the EM-Algorithm. See Mclust reference initialization See Mclust reference warn See Mclust reference... See Mclust reference
4 4 ridgeline.clust Value A list of 2 Objects. Mclust Merged Result of the Mclust-algorithm. See Mclust reference. Result of the merging-algorithm. It is a list with following components: Merged$klass Vector of cluster-lables for each datapoint Merged$info A kx3 matrix, where k is the number of merging iterations. i th row of this matrix consists of the numbers of the merged components a iteration i and the ridgeline-ratio of this components. For example info[1,] = c(1,2,0.8) means that in the first iteration components 1 and 2 were merged and their ridgline-ratio was 0.8. The new component gets the number 1 (the lesser one) Merged$diecluster A list the i-th element of which contains the numbers of old-clusters which were merged and build now the i th cluster. Merged$new_posteriors A N x M Matrix, where N is the number of datapoints and M is the number of clusters, which contains posteriori-probabilities, for each cluster and datapoint. Author(s) Grigory Alexandrovich References Alexandrovich G. (2010): Analytische Eigenschaften von Mischungen elliptischer Verteilungen und deren Anwendung in der Clusteranalyse, diploma thesis, Marburg University Fraley, C., Raftery, A.E. (2006): Technical Report MCLUST Version 3 for R. Hennig, C. (2009): Methods for merging Gaussian mixture components, Research Report no. 302, Department of Statistical Science, UCL Surajit Ray and Bruce G. Lindsay (2005): The topography of multivariate normal mixtures, Ann. Statist. 33, Number 5, pp See Also Mclust plot.ridgeclust Examples library(mvtnorm) #Simulate 1000 points from a gaussian mixture of 5 components: #Defining the means mu_vek <- array(dim=c(2,5)) mu_vek[,1] <- c(4,5) mu_vek[,2] <- c(1.5,5) mu_vek[,3] <- c(2,4.5) mu_vek[,4] <- c(4.1,1) mu_vek[,5] <- c(5,1) #Defining the covariances sigma_vek <- array(dim=c(2,2,5))
5 ridgeline.clust 5 sigma_vek[,,1] <- rbind(c(0.3,0.05),c(0.05,.3)) sigma_vek[,,2] <- rbind(c(0.1,0.05),c(0.05,.1)) sigma_vek[,,3] <- rbind(c(0.2,0),c(0,0.2)) sigma_vek[,,4] <- rbind(c(0.2,.1),c(.1,.2)) sigma_vek[,,5] <- rbind(c(.2,.1),c(.1,.2)) #Defining the weights weight <- c(0.2,0.25,0.15,0.2,0.2) #Simulation loop ind <- 1 dat <- array(dim=c(1000,2)) while(ind <= 1000) { component <- rmultinom(1,1,weight) component <- which(component == 1) dat[ind,] <- rmvnorm(1, mean = mu_vek[,component], sigma = sigma_vek[,,component]) ind <- ind + 1 } #Clustering result <- ridgeline.clust(dat) #Plot plot(result,dat)
6 Index Topic Plot clusters plot.ridgeclust, 1 Topic RidgeClust plot.ridgeclust, 1 Topic merging ridgeline.clust, 3 Topic model based clustering ridgeline.clust, 3 Mclust, 4 plot (plot.ridgeclust), 1 plot.ridgeclust, 1, 3, 4 RidgeClust (RidgeClustPackage), 2 RidgeClustPackage, 2 ridgeline.clust, 2, 3, 3 6
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