predict {arules} | R Documentation |
Provides the S4 method
predict
for itemMatrix
(e.g., transactions).
Predicts the membership (nearest neighbor) of new data to
clusters represented by
medoids or labeled examples.
## S4 method for signature 'itemMatrix': predict(object, newdata, labels = NULL, blocksize = 200,...)
object |
medoids (no labels needed) or examples (labels needed). |
newdata |
objects to predict labels for. |
labels |
"numeric" ; labels for the examples in object . |
blocksize |
"numeric" ; how much memory can predict
use for big x and/or y (approx. in MB). This is only a
crude approximation for 32-bit machines (64-bit architectures need
double the blocksize in memory) and using the
default Jaccard method for dissimilarity calculation.
In general, reducing blocksize will decrease
the memory usage but will increase the run-time. |
... |
further arguments passed on to dissimilarity . E.g.,
method . |
An integer vector of the same length as newdata
containing the predicted labels for each element.
dissimilarity
,
itemMatrix-class
data("Adult") ### sample small <- sample(Adult, 500) large <- sample(Adult, 5000) ### cluster a small sample d_jaccard <- dissimilarity(small) hc <- hclust(d_jaccard) l <- cutree(hc, k=4) ### predict labels for a larger sample labels <- predict(small, large, l) ### plot the profile of the 1. cluster itemFrequencyPlot(large[labels==1, itemFrequency(large) > 0.1])