Data Science Desktop Survival Guide by Graham Williams
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Biclustering

library(biclust)
tds <- matrix(rbinom(400, 50, 0.4), 20, 20)
res <- biclust(tds, method=BCCC(), delta=1.5, alpha=1, number=10)
res

## 
## An object of class Biclust 
## 
## call:
## 	biclust(x=tds, method=BCCC(), delta=1.5, alpha=1, number=10)
## 
....

bicluster(tds, res)

## $Bicluster1
##      [,1] [,2] [,3] [,4] [,5]
## [1,]   20   22   19   21   24
## [2,]   16   20   17   20   22
## [3,]   16   20   17   17   17
## [4,]   19   22   19   22   20
....

parallelCoordinates(tds, res, number=4)

data(BicatYeast)
tds <- discretize(BicatYeast)
res <- biclust(tds, method=BCXmotifs(), alpha=0.05, number=50)
res

## 
## An object of class Biclust 
## 
## call:
## 	biclust(x=tds, method=BCXmotifs(), alpha=0.05, number=50)
## 
....

parallelCoordinates(BicatYeast, res, number=4)

plotclust(res, tds)

tds <- tribble(~x, ~y,
               1, 1,
               2, 1,
               1, 0,
               4, 7,
               3, 5,
               3, 6)

res <- biclust(as.matrix(tds), method=BCCC(), delta=50, alpha=0, number=5)
res

## 
## An object of class Biclust 
## 
## call:
## 	biclust(x=as.matrix(tds), method=BCCC(), delta=50, alpha=0, 
## 	    number=5)
....