Hierarchical Clustering
Explore hierarchical clustering using the agglomerative approach in scikit-learn. Understand differences from K-means clustering, especially when data does not fit spherical assumptions. Learn to implement agglomerative clustering for flexible and accurate data grouping without relying on centroids or shape constraints.
We'll cover the following...
We'll cover the following...
Chapter Goals:
- Learn about hierarchical clustering using the agglomerative approach
A. K-means vs. hierarchical clustering
A major assumption that the K-means clustering algorithm makes is that the dataset consists of spherical (i.e. circular) clusters. With this assumption, the K-means algorithm will create clusters of data observations that are circular around the centroids. However, real life data often does ...