I have a bunch of texts written by the same person, and I'm trying to estimate the templates they use for each text. The way I'm going about this is:
TermDocumentMatrix
for all the textsX
distance (10 for the sake of argument)I'm able to get to the point of having the distance pairs, but I am unable to convert the dist
instance to something I can work with. There is a reproducible example at the bottom.
The data in the dist
instance looks like this:
The row and column names correspond to indexes in the original list of texts which I can use to do achieve step 5.
What I have been trying to get out of this is a sparse matrix with col name, row name, value
.
col, row, value
1 2 14.966630
1 3 12.449900
1 4 13.490738
1 5 12.688578
1 6 12.369317
2 3 12.449900
2 4 13.564660
2 5 12.922848
2 6 12.529964
3 4 5.385165
3 5 5.830952
3 6 5.830952
4 5 7.416198
4 6 7.937254
5 6 7.615773
From this point I would be comfortable cutting out all pairs greater than my cutoff and flattening the forest, i.e. returning 3 templates in this example, a group containing only document 1, a group containing only document 2 and a third group containing documents 3, 4, 5, and 6.
I have tried a bunch of things from creating a matrix out of this and then trying to make it sparse, to directly using the vector inside of the dist
class, and I just can't seem to figure it out.
Reproducible example:
tdm <- matrix(c(1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,1,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,3,1,2,2,2,3,2,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,1,2,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,4,1,1,1,1,1,0,0,1,1,1,1,0,1,0,0,0,0,0,0,1,1,1,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,1,1,1,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,2,0,0,1,0,1,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,1,1,0,1,1,1,1,0,1,0,0,0,0,0,0,1,1,1,1,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,1,0,0,1,1,1,1,0,1,0,1,0,0,2,0,0,0,0,0,1,0,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,3,1,1,1,1,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,1,1,1,0,0,1,1,1,1,0,0,0,1,0,0,2,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,3,1,1,1,1,0,1,0,0,0,0,1,2,0,1,1,0,0,0,0,1,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,1,1,1,1,0,1,0,0,0,0,0,0,0,1,0,0,1,1,1,1,1,1,0,0,0,0,1,0,0,1,0,1,0,0,0,0,1,1,1,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,1,1,0,1,0,0,0,0,0,1,1,1,2,1,1,1,0,0,0,0,1,2,2,1,1,1,1,0,1,0,0,0,0,0,1,0,0,0,0,0,1,1,1,1,1,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,1,1,1,0,2,0,0,0,0,0,0,1,1,1,1,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,2,0,2,2,3,2,1,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,1,1,1,1,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,2,1,1,1,1,1,0,1,0,0,0,0,1,1,0,0,0,0,1,0,0,0,1,0,0,1,1,1,1,1,1,0,0,0,0,0,1,0,0,0,0,1,0,1,1,1,1,1,0,0,1,1,1,0,0,1,0,0,0,0,0,0,1,1,1,1,1,0,0,0,0,0,0,0,1,2,1,1,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,1,0,2,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,3,0,1,1,1,1,0,0,1,0,1,1,1,0,0,0,0,0,1,0,0,0,0,0,4,2,4,6,4,3,1,0,1,2,1,1,0,1,0,0,0,0,2,0,0,0,0,0,0,1,1,1,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,2,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,1,1,1,1,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,2,1,2,2,2,2,1,0,1,2,1,1,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,0,0,0,0,0,2,2,2,2,2,2,3,3,4,5,3,1,2,1,1,1,1,1,1,0,0,0,0,3,3,0,0,1,1,0,1,0,0,0,0), nrow=6)
rownames(tdm) <- 1:6
colnames(tdm) <- paste("term", 1:229, sep="")
tdm.dist <- dist(tdm)
# I'm stuck turning tdm.dist into what I have shown
A classic approach to turn a "matrix"-like object to a [row, col, value] "data.frame" is the as.data.frame(as.table(.))
route. Specifically here, we need:
subset(as.data.frame(as.table(as.matrix(tdm.dist))), as.numeric(Var1) < as.numeric(Var2))
But that includes way too many coercions and creation of a larger object only to be subset immediately.
Since dist
stores its values in a "lower.tri"angle form we could use combn
to generate the row/col indices and cbind
with the "dist" object:
data.frame(do.call(rbind, combn(attr(tdm.dist, "Size"), 2, simplify = FALSE)), c(tdm.dist))
Also, "Matrix" package has some flexibility that, along its memory efficiency in creating objects, could be used here:
library(Matrix)
tmp = combn(attr(tdm.dist, "Size"), 2)
summary(sparseMatrix(i = tmp[2, ], j = tmp[1, ], x = c(tdm.dist),
dims = rep_len(attr(tdm.dist, "Size"), 2), symmetric = TRUE))
Additionally, among different functions that handle "dist" objects,
cutree(hclust(tdm.dist), h = 10)
#1 2 3 4 5 6
#1 2 3 3 3 3
groups by specifying the cut height.