Brief information from the UC Irvine Machine Learning Repository:
- Donated by Stefan Aeberhard
- Using chemical analysis determine the origin of wines
- 13 attributes (all continuous), 3 classes, no missing values
- 178 instances
- Ftp Access
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
Further information from one of the authors of the wine database:
Date: Mon, 9 Mar 1998 15:44:07 GMT To: michael@radwin.org From: riclea@crazy.anchem.unige.it (Riccardo Leardi) Subject: wines Dear Michael, I'm one of the authors of PARVUS. I saw your site and the reference to the data set wines. Therefore, I think it could be interesting for you to know the names of the variables (from the 27 original ones): 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 If you are interested, I can send you by snail mail the original paper (please send me your address). Best regards Riccardo Leardi