Clustering Analysis and Dimensionality Reduction
Clustering techniques aim to uncover concealed patterns or groupings within a dataset. These algorithms detect groupings without relying on any predefined labels. Instead, they select clusters based on the similarity between elements. Dimensionality reduction, on the other hand, involves transforming a dataset with numerous variables into one with fewer dimensions while preserving relevant information. Feature selection methods attempt to identify a subset of the original variables, while feature extraction reduces data dimensionality by transforming it into new features. This chapter shows us how to divide data into clusters, or groupings of similar items. We’ll also learn how to select features that best represent the set of data.
In this chapter, we will cover the following main topics:
- Understanding clustering – basic concepts and methods
- Understanding hierarchical clustering
- Partitioning-based clustering...
