Perform Principal component analysis and perform clustering using first 3 principal component scores (both Heirarchical and k mean clustering(scree plot or elbow curve) and obtain optimum number of clusters and check whether we have obtained same number of clusters with the original data (class column we have ignored at the begining who shows it has 3 clusters)
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.
Number of Attributes: 13 numeric, predictive attributes and the class Attribute Information: Alcohol Malic acid Ash Alcalinity of ash Magnesium Phenols Flavanoids Nonflavanoid phenols Proanthocyanins Color intensity Hue Dilution Proline