KEEL: A software tool to assess evolutionary algorithms for Data Mining problems (regression, classification, clustering, pattern mining and so on)

KEEL-dataset - data set description

Wine data set

Description
Files and additional references

Description

This section describes main characteristics of the wine data set and its attributes:

General information

Wine data set
Type	Classification	Origin	Real world
Features	13	(Real / Integer / Nominal)	(13 / 0 / 0)
Instances	178	Classes	3
Missing values?			No

Attribute description

Attribute	Domain	Attribute	Domain
Alcohol	[11.0, 14.9]	Nonflavanoid_phenols	[0.1, 0.7]
Malic_acid	[0.7, 5.8]	Proanthocyanins	[0.4, 3.6]
Ash	[1.3, 3.3]	Color_intensity	[1.2, 13.0]
Alcalinity_of_ash	[10.6, 30.0]	Hue	[0.4, 1.8]
Magnesium	[70.0, 162.0]	OD280/OD315	[1.2, 4.0]
Total_phenols	[0.9, 3.9]	Proline	[278.0, 1680.0]
Flavanoids	[0.3, 5.1]	Class	{1,2,3}

Additional information

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 wines.

In a classification context, this is a well posed problem with well behaved class structures. A good data set for first testing of a new classifier, but not very challenging.

Files and additional references

In this section you can download some files related to the wine data set:

The complete data set already formatted in KEEL format can be downloaded from here .
A copy of the data set already partitioned by means of a 10-folds cross validation procedure can be downloaded from here .
A copy of the data set already partitioned by means of a 5-folds cross validation procedure can be downloaded from here .
The header file associated to this data set can be downloaded from here .
This is not a native data set from the KEEL project. It has been obtained from the UCI Machine Learning Repository. The original page where the data set can be found is: http://archive.ics.uci.edu/ml/datasets/Wine.