2-3 Wine Dataset

[chinese][english]

Wine dataset collects data of 3 classes of wine from various places at Italy. Some characteristics are listed below:

• Data size: 178 entries
• 3 classes
• Data distribution: 59, 71, and 48 entries for each class
• 13 features corresponding to the values from chemical analysis, no missing data:
  • Alcohol
  • Malic acid
  • Ash
  • Alcalinity of ash
  • Magnesium
  • Total phenols
  • Flavanoids
  • Nonflavanoid phenols
  • Proanthocyanins
  • Color intensity
  • Hue
  • OD280/OD315 of diluted wines
  • Proline

Here are two papers that use the Wine dataset:

Wine 資料集也常被用在樣式辨認研究中，此資料集包含義大利在不同地點所生產之三種葡萄酒的資料，特性如下：

• 資料筆數：共178筆
• 特徵：共 13 種，都是經由化學分析所得到的數值，沒有未知量：
  • Alcohol
  • Malic acid
  • Ash
  • Alcalinity of ash
  • Magnesium
  • Total phenols
  • Flavanoids
  • Nonflavanoid phenols
  • Proanthocyanins
  • Color intensity
  • Hue
  • OD280/OD315 of diluted wines
  • Proline
• 類別：共三類，各含 59, 71, 48 筆資料。

有關於使用 wine 資料集的論文，列舉兩篇如下：

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).

   Abstract!G

   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).

We can display the data sizes among all classes, as follows:

我們可以計算每一個類別的資料量，如下：

Example 1: wineClassDataCount01.m

DS=prData('wine'); [classSize, classLabel]=dsClassSize(DS, 1); 13 features 178 instances 3 classes

We can display the feature distributions over different classes, as follows:

我們可以計算每一個類別的特徵分布圖，如下：

Example 2: wineClassDist01.m

DS=prData('wine'); dsDistPlot(DS);

We can plot the classes w.r.t. each of the features:

我們可以進行類別對單一特徵的作圖，如下：

Example 3: wineProjPlot1.m

DS = prData('wine'); dsProjPlot1(DS);