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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) .... ```

Other online resources include the GNU/Linux Desktop Survival Guide.
Books available on Amazon include Data Mining with Rattle and Essentials of Data Science.
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