Towards Automated Configuration of Stream Clustering Algorithms

Matthias Carnein, Heike Trautmann, Albert Bifet, Bernhard Pfahringer

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

3 Citations (Scopus)


Clustering is an important technique in data analysis which can reveal hidden patterns and unknown relationships in the data. A common problem in clustering is the proper choice of parameter settings. To tackle this, automated algorithm configuration is available which can automatically find the best parameter settings. In practice, however, many of our today’s data sources are data streams due to the widespread deployment of sensors, the internet-of-things or (social) media. Stream clustering aims to tackle this challenge by identifying, tracking and updating clusters over time. Unfortunately, none of the existing approaches for automated algorithm configuration are directly applicable to the streaming scenario. In this paper, we explore the possibility of automated algorithm configuration for stream clustering algorithms using an ensemble of different configurations. In first experiments, we demonstrate that our approach is able to automatically find superior configurations and refine them over time.
Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases
Subtitle of host publicationInternational Workshops of ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part I
Number of pages7
ISBN (Electronic)978-3-030-43823-4
ISBN (Print)978-3-030-43822-7
Publication statusPublished - 2020
Externally publishedYes
EventEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML-PKDD 2019 - Hubland campus University of Würzburg, Würzburg, Germany
Duration: 16 Sep 201920 Sep 2019

Publication series

NameCommunications in Computer and Information Science


ConferenceEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML-PKDD 2019
Abbreviated titleECML-PKDD 2019
Internet address


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