Clustering (Unsupervised Learning): Clustering is a technique used in unsupervised machine learning to group data points based on their inherent similarities, without any prior labels or categorizations. The main goal is to partition a dataset into clusters where items within a cluster are more alike to each other than to items in other clusters. This method helps uncover hidden patterns within data, making it particularly valuable when we don’t have any predefined categories or when we want to discover new insights from the data.

 

Hierarchical Clustering: Hierarchical clustering creates a tree of clusters. Unlike K-means, we don’t need to specify the number of clusters upfront. The method starts by treating each data point as a single cluster and then continually merges the closest pairs of clusters until only one large cluster remains. The result is a tree-like diagram called a dendrogram, which gives a multi-level hierarchy of clusters. One can then decide the number of clusters by cutting the dendrogram at a desired level. Hierarchical clustering is great for smaller datasets and when we want to understand hierarchical relationships, but it can be computationally intensive for larger datasets.