FISHDBC: Flexible, Incremental, Scalable, Hierarchical Density-Based Clustering for Arbitrary Data and Distance
This provides a practical solution for researchers and practitioners needing to cluster non-numeric or complex data without feature extraction, though it is incremental as it builds on existing density-based methods like HDBSCAN*.
The paper tackles the problem of clustering arbitrary data with custom distance functions by introducing FISHDBC, a flexible, incremental, scalable, and hierarchical density-based algorithm that approximates HDBSCAN* and avoids O(n^2) complexity, enabling efficient updates with lightweight computation.
FISHDBC is a flexible, incremental, scalable, and hierarchical density-based clustering algorithm. It is flexible because it empowers users to work on arbitrary data, skipping the feature extraction step that usually transforms raw data in numeric arrays letting users define an arbitrary distance function instead. It is incremental and scalable: it avoids the $\mathcal O(n^2)$ performance of other approaches in non-metric spaces and requires only lightweight computation to update the clustering when few items are added. It is hierarchical: it produces a "flat" clustering which can be expanded to a tree structure, so that users can group and/or divide clusters in sub- or super-clusters when data exploration requires so. It is density-based and approximates HDBSCAN*, an evolution of DBSCAN.