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葡萄酒数据集

葡萄酒数据集

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Physical Classification

Original Owners:Forina, M. et al, PARVUS - An Extendible Package for Data Exploration, Classification and Correlation. I......

数据结构 ? 5K

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    README.md

    Original Owners:

    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.

    Donor:

    Stefan Aeberhard, email: stefan '@' coral.cs.jcu.edu.au


    Data Set 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

    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.


    Attribute Information:

    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)


    Relevant Papers:

    (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)



      (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.


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