Comparing Hierarchical, K-Means, and DBSCAN Clustering Algorithms in Python

Hierarchical Clustering

Hierarchical clustering builds a tree-like structure of nested clusters, starting with individual data points as separate clusters and then merging them iteratively. It doesn’t require specifying the number of clusters in advance, making it flexible. Hierarchical clustering can be visualized as a dendrogram, which is useful for understanding the hierarchy of clusters.

K-Means Clustering

K-Means is a partitioning algorithm that divides data points into K clusters. It works by assigning each data point to the nearest cluster centroid and updating the centroids until convergence. K-Means is sensitive to the initial choice of centroids and may not handle irregularly shaped clusters well.

DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

DBSCAN groups data points based on their density. It identifies dense regions as clusters and marks data points with low density as noise or outliers. DBSCAN can discover clusters of arbitrary shapes and…