| 
| 1. Title of Database: Wine recognition data
| 2. Sources:
|    (a) Forina, M. et al, PARVUS - An Extendible Package for Data
|        Exploration, Classification and Correlation. Institute of Pharmaceutical
|        and Food Analysis and Technologies, Via Brigata Salerno, 
|        16147 Genoa, Italy.
| 
|    (b) Stefan Aeberhard, email: stefan@coral.cs.jcu.edu.au
|    (c) July 1991
| 3. Past Usage:
| 
|    (1)
|    S. Aeberhard, D. Coomans and O. de Vel,
|    Comparison of Classifiers in High Dimensional Settings,
|    Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of
|    Mathematics and Statistics, James Cook University of North Queensland.
|    (Also submitted to Technometrics).
| 
|    The data was used with many others for comparing various 
|    classifiers. The classes are separable, though only RDA 
|    has achieved 100% correct classification.
|    (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data))
|    (All results using the leave-one-out technique)
| 
|    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.
| 
|    (2) 
|    S. Aeberhard, D. Coomans and O. de Vel,
|    "THE CLASSIFICATION PERFORMANCE OF RDA"
|    Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of
|    Mathematics and Statistics, James Cook University of North Queensland.
|    (Also submitted to Journal of Chemometrics).
| 
|    Here, the data was used to illustrate the superior performance of
|    the use of a new appreciation function with RDA. 
| 
| 4. Relevant 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. 
| 
|    -- I think that the initial data set had around 30 variables, but 
|       for some reason I only have the 13 dimensional version. 
|       I had a list of what the 30 or so variables were, but a.) 
|       I lost it, and b.), I would not know which 13 variables
|       are included in the set.
| 
| 5. Number of Instances
| 
|       class 1 59
| 	class 2 71
| 	class 3 48
| 
| 6. Number of Attributes 
| 	
| 	13
| 
| 7. For Each Attribute:
| 
| 	All attributes are continuous
| 	
| 	No statistics available, but suggest to standardise
| 	variables for certain uses (e.g. for us with classifiers
| 	which are NOT scale invariant)
| 
| 	NOTE: 1st attribute is class identifier (1-3)
| 
| 8. Missing Attribute Values:
| 
| 	None
| 
| 9. Class Distribution: number of instances per class
| 
|       class 1 59
| 	class 2 71
| 	class 3 48
| 

1,2,3

c1: continuous.
c2: continuous.
c3: continuous.
c4: continuous.
c5: continuous.
c6: continuous.
c7: continuous.
c8: continuous.
c9: continuous.
c10: continuous.
c11: continuous.
c12: continuous.
c13: continuous.