Hierarchical Clustering is an unsupervised machine learning method used to group data points into clusters based on their similarity, forming a hierarchy of nested clusters. It is commonly used in data mining, bioinformatics, and market segmentation to uncover patterns in unlabeled datasets.
The technique builds a dendrogram—a tree-like structure—where each branch represents a cluster and the height indicates the distance at which clusters merge. It does not require specifying the number of clusters in advance, making it useful for exploratory data analysis. Two main approaches exist: agglomerative (bottom-up) and divisive (top-down), with agglomerative being more commonly used.
- Applies distance metrics such as Euclidean or Manhattan distance to measure similarity
- Uses linkage criteria like single, complete, or average linkage to determine cluster fusion
- Relies on algorithms such as UPGMA or Ward’s method for cluster formation
- Commonly implemented using Python (scikit-learn, scipy) or R
- Used in fields like genomics, customer segmentation, and image analysis
Professionals skilled in Hierarchical Clustering are expected to preprocess data, choose appropriate distance and linkage methods, interpret dendrograms, and validate cluster quality. They often work in data science, research, and analytics roles across industries including healthcare, finance, and e-commerce. Mastery includes understanding scalability limitations and comparing results with other clustering techniques like k-means.