# Info on Wine Recognition Data Base

    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