predict

0th

Percentile

Model Predictions

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.

Keywords
models, cluster
Usage
# S4 method for itemMatrix
predict(object, newdata, labels = NULL, blocksize = 200,…)
Arguments
object

medoids (no labels needed) or examples (labels needed).

newdata

objects to predict labels for.

labels

an integer vector containing the labels for the examples in object.

blocksize

a numeric scalar indicating how much memory predict can 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.

Value

An integer vector of the same length as newdata containing the predicted labels for each element.

See Also

dissimilarity, itemMatrix-class

Aliases
  • predict
  • predict,itemMatrix-method
Examples
# NOT RUN {
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])
# }
Documentation reproduced from package arules, version 1.5-4, License: GPL-3

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