predict {arules} | R Documentation

## 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.

### See Also

`dissimilarity`, `itemMatrix-class`

### 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])

[Package arules version 0.6-3 Index]