👉 Unagglomerative clustering is a type of unsupervised machine learning algorithm that identifies patterns in data without explicit labels. It's an iterative method where each cluster is defined by its own features (e.g., distance, size) and then it is merged back together to form a complete group. The goal is to find the most similar clusters for new observations based on these features. The term "unagglomerative" refers to the fact that this algorithm does not require explicit labels or a reference
