# FISHDBC: Flexible, Incremental, Scalable, Hierarchical Density-Based Clustering for Arbitrary Data and Distance

arXiv: https://arxiv.org/abs/1910.07283 · HTML: https://mlsanity.com/paper/1910.07283/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2019.

## Built on by (most cited first, 4 of 4)

- [Carbon Filter: Real-time Alert Triage Using Large Scale Clustering and Fast Search](https://mlsanity.com/paper/2405.04691/) (2024)
- [Sonic: Fast and Transferable Data Poisoning on Clustering Algorithms](https://mlsanity.com/paper/2408.07558/) (2024)
- [Dynamic data summarization for hierarchical spatial clustering](https://mlsanity.com/paper/2412.07789/) (2024)
- [FAMST: Fast Approximate Minimum Spanning Tree Construction for Large-Scale and High-Dimensional Data](https://mlsanity.com/paper/2507.14261/) (2025)

## Abstract (arXiv)

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(n2) 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.