predict: Model Predictions

In arules: Mining Association Rules and Frequent Itemsets

Model Predictions

Description

Provides the method predict() for itemMatrix (e.g., transactions). Predicts the membership (nearest neighbor) of new data to clusters represented by medoids or labeled examples.

Usage

predict(object, ...)

## S4 method for signature 'itemMatrix'
predict(object, newdata, labels = NULL, blocksize = 200, ...)

Arguments

object	clustered examples or medoids stored as an itemMatrix.
...	further arguments passed on to dissimilarity(). E.g., method.
newdata	an itemMatrix containing the objects to predict labels for.
labels	a vector containing one label for each row in object. If NULL, the row numbers of object are used.
blocksize	approximate maximum memory, in MB, used for the cross-dissimilarity matrix. Reducing blocksize lowers peak memory use but can increase run time.

Details

object and newdata must use compatible item coding (the same items in the same order). For each row in newdata, the label of the least dissimilar row in object is returned. Ties are resolved by max.col() and may therefore be selected at random.

Value

A vector with one predicted label per row of newdata. Its values come from labels (or from the row numbers of object when labels = NULL).

Author(s)

Michael Hahsler

See Also

Other proximity classes and functions: affinity(), dissimilarity(), proximity-classes

Examples

data("Adult")

## sample
set.seed(1234)
small <- sample(Adult, 500)
large <- sample(Adult, 5000)

## cluster a small sample and extract the cluster label vector
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 documentation built on Sept. 11, 2026, 9:08 a.m.