Introduction
Unsupervised learning models find patterns in unlabeled data. Clustering is a technique for finding groups of objects such that the objects in a group are like one another, yet objects in different groups are dissimilar. Principal component analysis (PCA) is a technique for reducing the dimensionality of data. We will discuss both techniques in the context of product clustering, which uses textual product descriptions to group similar products together.
In this chapter, we will:
- Discuss two unsupervised learning techniques: clustering and principal component analysis.
- Use the K-means clustering algorithm.
