Feature Engineering for Modern Machine Learning with Scikit-Learn

2. Advanced Clustering Techniques

While K-means clustering is effective for many customer segmentation tasks, it has limitations, particularly with data that isn't well-separated or contains non-spherical clusters. In such cases, alternative methods like Hierarchical Clustering and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) can offer better segmentation. These techniques adapt to various data structures, allowing for more flexibility in discovering meaningful clusters.

Hierarchical Clustering, for instance, creates a tree-like structure of nested clusters, which can be particularly useful when the number of clusters is not known in advance. This method allows for a more nuanced understanding of how data points relate to ...