clustersis

Clustersis is a clustering framework used in data analysis to identify groups in complex data sets. It combines density-based ideas, which detect high-density regions, with centroid- or prototype-based ideas that emphasize global cohesion. The resulting method handles clusters of irregular shape and varying size in high-dimensional spaces.

Origin and terminology: The term clustersis was introduced to describe this integrated approach in clustering literature. It signals an effort to synthesize local density structure with global cluster prototypes. Proponents emphasize its robustness to noise and its ability to separate overlapping groups.

Method: Clustersis typically estimates a local density for each point and assigns points to clusters by minimizing a joint objective that blends density reachability with a cohesion term based on prototypes. An optional hierarchical refinement merges or splits clusters using a stability criterion tested across subsamples or bootstrap resamples. The number of clusters can be chosen via a stability score or an elbow-like heuristic.

Variants and implementation: Variants include clustersis-DBSCAN hybrids that use a density radius and clustersis-HAC hybrids that apply hierarchical agglomeration to prototypes. Implementations focus on scalable approximations and parallel computation to handle large data sets.

Applications and evaluation: Clustersis has been applied to genomics, image segmentation, text clustering, and market research. Like other clustering methods, results depend on parameter choices and data quality. Evaluation typically uses internal validity indices, stability under subsampling, and comparison with known labels when available.