wine

<p>These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three
different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wine: Barolo, Grignolino, Barbera. 
The data set is used to evaluate the <code>pdfCluster</code> ability of understanding the type of wine, given the chemical measurement.</p>

datasets

Cluster analysis via nonparametric density
estimation is performed. Operationally, the kernel method is used throughout to estimate
the density. Diagnostics methods for evaluating the quality of the clustering
are available. The package includes also a routine to estimate the
probability density function obtained by the kernel method, given a set of
data with arbitrary dimensions.

Giovanna Menardi

pdfCluster

Cluster Analysis via Nonparametric Density Estimation

Menardi Giovanna

Azzalini Adelchi

Rosolin Tiziana

wine function

<p>This data frame contains 178 rows, each corresponding to a different cultivar of wine produced in Piedmont (Italy), and 14 columns. 
The first column is the type of wine, a factor variable with the following levels: Barolo, Grignolino, Barbera.
The variables measured on the three types of wines are the following: Alcohol, Malic acid, Ash, Alcalinity, Magnesium, Phenols, Flavanoids,
Nonflavanoids, Proanthocyanins, Color intensity, Hue, OD280.OD315Dilution, Proline. All variables but the label class are continuous.</p>

Format

Wine data — wine

Wine data

wine: Wine data

Description

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Details