riem.hclust: Hierarchical Agglomerative Clustering In Riemann: Learning with Data on Riemannian Manifolds

Description

Given N observations X_1, X_2, …, X_M \in \mathcal{M}, perform hierarchical agglomerative clustering with fastcluster package's implementation.

Usage

 1 2 3 4 5 6 7 riem.hclust( riemobj, geometry = c("intrinsic", "extrinsic"), method = c("single", "complete", "average", "mcquitty", "ward.D", "ward.D2", "centroid", "median"), members = NULL ) 

Arguments

 riemobj a S3 "riemdata" class for N manifold-valued data. geometry (case-insensitive) name of geometry; either geodesic ("intrinsic") or embedded ("extrinsic") geometry. method agglomeration method to be used. This must be one of "single", "complete", "average", "mcquitty", "ward.D", "ward.D2", "centroid" or "median". members NULL or a vector whose length equals the number of observations. See hclust for details.

Value

an object of class hclust. See hclust for details.

hclust
  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 #------------------------------------------------------------------- # Example on Sphere : a dataset with three types # # class 1 : 10 perturbed data points near (1,0,0) on S^2 in R^3 # class 2 : 10 perturbed data points near (0,1,0) on S^2 in R^3 # class 3 : 10 perturbed data points near (0,0,1) on S^2 in R^3 #------------------------------------------------------------------- ## GENERATE DATA mydata = list() for (i in 1:10){ tgt = c(1, stats::rnorm(2, sd=0.1)) mydata[[i]] = tgt/sqrt(sum(tgt^2)) } for (i in 11:20){ tgt = c(rnorm(1,sd=0.1),1,rnorm(1,sd=0.1)) mydata[[i]] = tgt/sqrt(sum(tgt^2)) } for (i in 21:30){ tgt = c(stats::rnorm(2, sd=0.1), 1) mydata[[i]] = tgt/sqrt(sum(tgt^2)) } myriem = wrap.sphere(mydata) ## COMPUTE SINGLE AND COMPLETE LINKAGE hc.sing <- riem.hclust(myriem, method="single") hc.comp <- riem.hclust(myriem, method="complete") ## VISUALIZE opar <- par(no.readonly=TRUE) par(mfrow=c(1,2)) plot(hc.sing, main="single linkage") plot(hc.comp, main="complete linkage") par(opar)