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

Glass Identification data set

Description
Files and additional references

Description

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

General information

Glass Identification data set
Type	Classification	Origin	Real world
Features	9	(Real / Integer / Nominal)	(9 / 0 / 0)
Instances	214	Classes	7
Missing values?			No

Attribute description

Attribute	Domain
RI	[1.51115, 1.53393]
Na	[10.73, 17.38]
Mg	[0.0, 4.49]
Al	[0.29, 3.5]
Si	[69.81, 75.41]
K	[0.0, 6.21]
Ca	[5.43, 16.19]
Ba	[0.0, 3.15]
Fe	[0.0, 0.51]
TypeGlass	{1, 2, 3, 4, 5, 6, 7}

Additional information

From USA Forensic Science Service; 6 types of glass which can be found in the crime scene, defined in terms of their oxide content (i.e. Na, Fe, K, etc).

Files and additional references

In this section you can download some files related to the glass 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/Glass+Identification.