In this chapter, we started to explore unsupervised learning techniques. We focused on cluster analysis to both provide data reduction and data understanding of the observations. Three methods were introduced: the traditional hierarchical and k-means clustering algorithms along with the Gower metric and PAM for mixed data. We applied these three methods to find a structure in Italian wines coming from three different cultivars and examined the results. In the next chapter, we will continue exploring unsupervised learning, but instead of finding structure among the observations, we will focus on finding structure among the variables in order to create new features that can be used in a supervised learning problem.