banknote

Data from Tables 1.1 and 1.2 (pp. 5-8) of Flury and Riedwyl
 (1988). There are six measurements made on 200 Swiss
 banknotes (the old-Swiss 1000-franc). The banknotes belong to two
 classes of equal size: genuine and counterfeit.

datasets

Performs robust cluster analysis allowing for outliers and noise that cannot be fitted by any cluster. The data are modelled by a mixture of Gaussian distributions and a noise component, which is an improper uniform distribution covering the whole Euclidean space. Parameters are estimated by (pseudo) maximum likelihood. This is fitted by a EM-type algorithm. See Coretto and Hennig (2016) <doi:10.1080/01621459.2015.1100996>, and Coretto and Hennig (2017) <https://jmlr.org/papers/v18/16-382.html>.

Pietro Coretto

otrimle

Robust Model-Based Clustering

Christian Hennig

banknote function

A <code>data.frame</code> of dimension <code>200x7</code> with the following variables:
<dl class="dl-horizontal">
 <dt>Class</dt><dd>a <code>factor</code> with classes: <code>genuine</code>, <code>counterfeit</code></dd>
 <dt>Length</dt><dd>Length of bill (mm)</dd>
 <dt>Left</dt><dd>Width of left edge (mm)</dd>
 <dt>Right</dt><dd>Width of right edge (mm)</dd>
 <dt>Bottom</dt><dd>Bottom margin width (mm)</dd>
 <dt>Top</dt><dd>Top margin width (mm)</dd>
 <dt>Diagonal</dt><dd>Length of diagonal (mm)</dd>
 </dl>

Format

Swiss Banknotes Data — banknote

A <code>data.frame</code> of dimension <code>200x7</code> with the following variables:
<dl class='dl-horizontal'>
 <dt>Class</dt><dd>a <code>factor</code> with classes: <code>genuine</code>, <code>counterfeit</code></dd>
 <dt>Length</dt><dd>Length of bill (mm)</dd>
 <dt>Left</dt><dd>Width of left edge (mm)</dd>
 <dt>Right</dt><dd>Width of right edge (mm)</dd>
 <dt>Bottom</dt><dd>Bottom margin width (mm)</dd>
 <dt>Top</dt><dd>Top margin width (mm)</dd>
 <dt>Diagonal</dt><dd>Length of diagonal (mm)</dd>
 </dl>

banknote: Swiss Banknotes Data

Description

Usage

Arguments

Format