R Under development (unstable) (2024-04-27 r86487 ucrt) -- "Unsuffered Consequences"
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> library(ClassDiscovery)
Loading required package: cluster
Loading required package: oompaBase
> suppressWarnings( RNGversion("3.5.3") )
> set.seed(593996)
> dd <- matrix(rnorm(100*5, rnorm(100)), nrow=100, ncol=5)
> distanceMatrix(dd, 'pearson')
          1         2         3         4
2 0.2076737                              
3 0.2695364 0.1801450                    
4 0.2333953 0.1927620 0.1724315          
5 0.2913239 0.2293372 0.2168545 0.2209858
> distanceMatrix(dd, 'euclid')
         1        2        3        4
2 13.68928                           
3 15.31751 12.94784                  
4 14.23804 13.43355 12.51983         
5 15.01881 13.96244 13.32337 13.21688
> distanceMatrix(dd, 'sqrt')
          1         2         3         4
2 0.6444745                              
3 0.7342157 0.6002415                    
4 0.6832208 0.6209058 0.5872504          
5 0.7633137 0.6772550 0.6585658 0.6648094
> distanceMatrix(dd, 'weird')
          1         2         3         4
2 0.6316359                              
3 0.7882128 0.5264999                    
4 0.7029820 0.5481066 0.4937517          
5 0.8321969 0.6762622 0.6302502 0.6460226
> distanceMatrix(dd, 'cosine')
          1         2         3         4
2 0.4153118                              
3 0.5390147 0.3600312                    
4 0.4701264 0.3907693 0.3510495          
5 0.5856946 0.4639313 0.4398581 0.4378273
> rm(dd) # cleanup
> 
> # simulate data from three different groups
> d1 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
> d2 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
> d3 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
> dd <- cbind(d1, d2, d3)
> 
> # perform hierarchical clustering using correlation
> hc <- hclust(distanceMatrix(dd, 'pearson'), method='average')
> cols <- rep(c('red', 'green', 'blue'), each=10)
> labs <- paste('X', 1:30, sep='')
> 
> # plot the dendrogram with color-coded groups
> plotColoredClusters(hc, labs=labs, cols=cols)
> 
> #cleanup
> rm(d1, d2, d3, dd, hc, cols, labs)
> 
> proc.time()
   user  system elapsed 
   0.21    0.09    0.29