R: Unsupervised Classification

classifyUnsup {RecordLinkage} | R Documentation

## Unsupervised Classification

### Description

Classify record pairs with unsupervised clustering methods.

### Usage

    classifyUnsup(rpairs, method, ...)

### Arguments

`rpairs` | Object of type `RecLinkData`. The data to classify.
`method` | The classification method to use. One of `"kmeans"`, `"bclust"`.
`...` | Further arguments for the classification method

### Details

A clustering algorithm is applied to find clusters in the comparison patterns. In the case of two clusters (the default), the cluster further from the origin (i.e. representing higher similarity values) is interpreted as the set of links, the other as the set of non-links.

Supported methods are:

kmeans

  K-means clustering, see `kmeans`.

bclust

  Bagged clustering, see `bclust`.

### Value

An object of class `"RecLinkResult"` that represents a copy of `newdata` with element `rpairs$prediction`, which stores the classification result, as addendum.

### Author(s)

Andreas Borg, Murat Sariyar

### See Also

`trainSupv` and `classifySupv` for supervised classification.

### Examples

    # Classification with bclust
    data(RLdata500)
    rpairs=compare.dedup(RLdata500, identity=identity.RLdata500,
                        blockfld=list(1,3,5,6,7))
    result=classifyUnsup(rpairs,method="bclust")
    summary(result)

[Package RecordLinkage version 0.4-11.2 Index]