Hcluster

Hcluster is a software library and framework for performing hierarchical clustering, a family of unsupervised learning methods that organize data into nested groups arranged in a dendrogram. The approach can reveal structure at multiple levels, from broad clusters to finer subgroups, by iteratively merging or splitting clusters based on a chosen similarity or distance measure.

The library supports both major strategies: agglomerative clustering, which starts with individual items and merges clusters step by step, and divisive clustering, which begins with the entire set and splits it into smaller clusters. Users can select common linkage criteria, such as single, complete, average, and Ward’s method, and choose distance metrics including Euclidean, Manhattan, cosine, and correlation-based distances. Hcluster can operate on raw feature data or use a precomputed distance or dissimilarity matrix, offering flexibility for various data types and scales.

Inputs and outputs: The primary input is a data matrix or distance matrix with items to be clustered. Optional preprocessing steps include normalization or scaling. The framework outputs a hierarchical model and a dendrogram, with the ability to extract flat cluster labels by cutting the tree at a specified height or number of clusters. Evaluation tools such as the cophenetic correlation coefficient and silhouette scores are often available to assess clustering quality.

Implementation and use: Hcluster is designed to integrate into data analysis pipelines and supports multiple programming environments, potentially including Python and R interfaces. It emphasizes modularity, scalability, and compatibility with large datasets through memory-efficient representations and, where applicable, parallel computation.

See also: hierarchical clustering, dendrogram, linkage. Note that the term Hcluster can refer to various locally developed implementations and is not a single canonical standard in the field.