Commit Inicial

data/tanveer/wine/wine.names

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+1. Title of Database: Wine recognition data
+	Updated Sept 21, 1998 by C.Blake : Added attribute information
+
+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.
+
+   -- The attributes are (dontated by Riccardo Leardi, 
+	riclea@anchem.unige.it )
+ 	1) Alcohol
+ 	2) Malic acid
+ 	3) Ash
+	4) Alcalinity of ash  
+ 	5) Magnesium
+	6) Total phenols
+ 	7) Flavanoids
+ 	8) Nonflavanoid phenols
+ 	9) Proanthocyanins
+	10)Color intensity
+ 	11)Hue
+ 	12)OD280/OD315 of diluted wines
+ 	13)Proline            
+
+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