BURST: Rendering Clustering Techniques Suitable for Evolving Streams

Apostolos Giannoulidis

doi:10.14778/3749646.3749675

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BURST: Rendering Clustering Techniques Suitable for Evolving Streams

2025-07-01

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Apostolos Giannoulidis *

Anastasios Gounaris

Twardzik, Erica

DOI：10.14778/3749646.3749675

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Abstract

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Identifying patterns or clusters in streaming time-series data is crucial for decision-making, and underpins applications such as anomaly detection, forecasting, and data quality monitoring. While numerous clustering algorithms have been proposed, many remain unexplored in the time-series domain, and others are unsuitable for streaming scenarios. Moreover, many effective methods require prior knowledge of the number of clusters, a significant limitation when dealing with evolving data streams. To address these challenges, we propose BURST, a principled and general-purpose framework that enables the application of partition-based clustering methods in streaming time-series settings. At its core, BURST integrates AutoKC, a novel, adaptive algorithm for automatically estimating the number of clusters, enhancing robustness to evolving time-series streams. Experimental analyses show that BURST is a robust strategy for real-time time-series clustering, effectively generalizing across different partitioning methods, and achieving state-of-the-art performance compared to existing algorithms.

Keywords:

ANOMALY DETECTION

EFFICIENT

REPRESENTATION