clustersdense

Clustersdense is a family of clustering approaches designed to identify and delineate dense regions in a multivariate data set, while treating sparsely populated regions as noise. It emphasizes local data density over global variance minimization, producing clusters that reflect natural groupings even when shapes are irregular or densities vary across the space.

The core idea is to estimate local density for each data point, using methods such as kernel density estimation or distance-based density (for example, k-nearest neighbors density). Points with density above a defined threshold are regarded as core points; non-core points are assigned to the cluster of the nearest higher-density neighbor if this neighbor lies within a connectivity radius. Clusters form from connected components of core points and their reachable border points.

Variants of clustersdense may employ dynamic density thresholds, density reachability graphs, or hierarchical, multi-resolution schemes to reveal clusters at different density levels. Parameter choices—bandwidth for density estimation, the density threshold, and the connectivity radius—affect sensitivity to noise, fragmentation, and the granularity of discovered clusters. In high dimensions, density estimation can be unreliable, and computational cost increases, so approximate methods and dimensionality reduction are often employed. Computationally efficient implementations rely on approximate neighbor search and sparse graph representations.

Relation to other methods: clustersdense shares goals with density-based clustering approaches such as DBSCAN and OPTICS but emphasizes explicit density estimation and density-based connectivity to guide cluster formation. It is applicable to spatial, temporal, and high-dimensional data and is commonly used in exploratory data analysis, pattern recognition, geospatial analysis, and anomaly detection when cluster density varies across the data set.