Unsupervised learning! Using Principal component analysis for dimension reduction and then clustering analysis.
This dataset is adapted from the Wine Data Set from https://archive.ics.uci.edu/ml/datasets/wine by removing the information about the types of wine for unsupervised learning.
The following descriptions are adapted from the UCI webpage:
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.
The attributes are: Alcohol Malic acid Ash Alcalinity of ash Magnesium Total phenols Flavanoids Nonflavanoid phenols Proanthocyanins Color intensity Hue OD280/OD315 of diluted wines Proline