Dynamic data summarization for hierarchical spatial clustering

TL;DR

This paper introduces a novel dynamic clustering algorithm that efficiently updates hierarchical spatial clusters in response to data changes, significantly improving runtime performance in dynamic environments.

Contribution

It presents an exact algorithm with a Bubble-tree structure and an online-offline framework for dynamic data summarization in hierarchical spatial clustering.

Findings

The method maintains clustering accuracy comparable to static techniques.

It significantly reduces runtime in dynamic data scenarios.

The approach effectively adapts to fully dynamic environments.

Abstract

Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) finds meaningful patterns in spatial data by considering density and spatial proximity. As the clustering algorithm is inherently designed for static applications, so have recent studies focused on accelerating the algorithm for static applications using approximate or parallel methods. However, much less attention has been given to dynamic environments, where even a single point insertion or deletion can require recomputing the clustering hierarchy from scratch due to the need of maintaining the minimum spanning tree (MST) over a complete graph. This paper addresses the challenge of enhancing the clustering algorithm for dynamic data. We present an exact algorithm that maintains density information and updates the clustering hierarchy of HDBSCAN during point insertions and deletions. Considering the hardness of adapting the exact algorithm to dynamic data involving modern workloads, we propose an online-offline framework. The online component efficiently summarizes dynamic data using a tree structure, called Bubble-tree, while the offline step performs the static clustering. Experimental results demonstrate that the data summarization adapts well to fully dynamic environments, providing compression quality on par with existing techniques while significantly improving runtime performance of the clustering algorithm in dynamic data workloads.