Description Usage Arguments Value benchmark.random benchmarks.kmeans_lda
These are wrapper to other methods for the clustering of count data. They can be used to initialize the clustering. It is also possible to implement your own benchmark function depending on other packages.
1 2 3 | benchmark.random(dtm, Q, ...)
benchmark.kmeans_lda(dtm, Q, K, nruns = 1, ...)
|
dtm |
an S4 object of class |
Q |
The number of clusters |
... |
Some argument to be consistent with the function's skeleton :
|
K |
Number of topics (dimension of the latent space). |
nruns |
Number of restart of the |
A vector of size equal to the number of row of dtm
, containing
a Q-clustering
benchmark.random
Random initialisation of the clustering. Arguments K and nruns are unused
benchmarks.kmeans_lda
Cluster the matrix theta obtained by a topicmodels LDA with K topics
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.