rclust.hclust: Hierarchical Agglomerative Clustering

In kisungyou/RiemCluster: Cluster Analysis on Manifolds

Description

Hierarchical Agglomerative Clustering

Usage

rclust.hclust(input, type = c("extrinsic", "intrinsic"),
  method = c("single", "complete", "average", "mcquitty", "ward.D",
  "ward.D2", "centroid", "median"), members = NULL)

Examples

## load point clouds of 3 groups on sphere
data(sphere3)

## visualize data on sphere
library("rgl")
mfrow3d(1,2)
spheres3d(0,0,0,lit=FALSE,color="white")
spheres3d(0,0,0,radius=1.01,lit=FALSE,color="black",front="lines")
spheres3d(sphere3[,1],sphere3[,2],sphere3[,3],col="black",radius=0.03)


## wrap the data into 'riemdata' class 
## note that when given as a matrix, each data be a column.
library(RiemBase)
rsp3 <- riemfactory(t(sphere3), name="Sphere")

## apply hierarchical clustering with complete linkage
result  <- rclust.hclust(rsp3, method="complete")

## get clustering labels under 3-group assumption
label3  <- stats::cutree(result, k=3)


## find indices corresponding to 3 groups
idx.gp1 <- which(label3==unique(label3)[1])
idx.gp2 <- which(label3==unique(label3)[2])
idx.gp3 <- which(label3==unique(label3)[3])

## visualize clustering results of 3 groups
rgl::next3d()
spheres3d(0,0,0,lit=FALSE,color="white")
spheres3d(0,0,0,radius=1.01,lit=FALSE,color="black",front="lines")
spheres3d(sphere3[idx.gp1,1],sphere3[idx.gp1,2],sphere3[idx.gp1,3],col="red",radius=0.03)
spheres3d(sphere3[idx.gp2,1],sphere3[idx.gp2,2],sphere3[idx.gp2,3],col="blue",radius=0.03)
spheres3d(sphere3[idx.gp3,1],sphere3[idx.gp3,2],sphere3[idx.gp3,3],col="yellow",radius=0.03)

## you can close the RGL figure by 'rgl.close()'

kisungyou/RiemCluster documentation built on May 17, 2019, 7:45 a.m.