arules: predict – R documentation

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predict

Model Predictions

Description

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.

Usage

## S4 method for signature '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.

Author(s)

Michael Hahsler

Examples

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])

arules

Mining Association Rules and Frequent Itemsets

v1.6-7

GPL-3

Authors

Michael Hahsler [aut, cre, cph], Christian Buchta [aut, cph], Bettina Gruen [aut, cph], Kurt Hornik [aut, cph], Ian Johnson [ctb, cph], Christian Borgelt [ctb, cph]

Initial release

2021-03-12