{"type":"conference","citation":{"ama":"Carnein M, Trautmann H, Bifet A, Pfahringer B. Towards Automated Configuration of Stream Clustering Algorithms. In: <i>Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)</i>. ; 2020:137–143. doi:<a href=\"https://doi.org/10.1007/978-3-030-43823-4_12\">10.1007/978-3-030-43823-4_12</a>","ieee":"M. Carnein, H. Trautmann, A. Bifet, and B. Pfahringer, “Towards Automated Configuration of Stream Clustering Algorithms,” in <i>Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)</i>, 2020, pp. 137–143, doi: <a href=\"https://doi.org/10.1007/978-3-030-43823-4_12\">10.1007/978-3-030-43823-4_12</a>.","bibtex":"@inproceedings{Carnein_Trautmann_Bifet_Pfahringer_2020, place={Würzburg, Germany}, title={Towards Automated Configuration of Stream Clustering Algorithms}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-43823-4_12\">10.1007/978-3-030-43823-4_12</a>}, booktitle={Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)}, author={Carnein, Matthias and Trautmann, Heike and Bifet, Albert and Pfahringer, Bernhard}, year={2020}, pages={137–143} }","short":"M. Carnein, H. Trautmann, A. Bifet, B. Pfahringer, in: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19), Würzburg, Germany, 2020, pp. 137–143.","apa":"Carnein, M., Trautmann, H., Bifet, A., &#38; Pfahringer, B. (2020). Towards Automated Configuration of Stream Clustering Algorithms. <i>Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)</i>, 137–143. <a href=\"https://doi.org/10.1007/978-3-030-43823-4_12\">https://doi.org/10.1007/978-3-030-43823-4_12</a>","chicago":"Carnein, Matthias, Heike Trautmann, Albert Bifet, and Bernhard Pfahringer. “Towards Automated Configuration of Stream Clustering Algorithms.” In <i>Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)</i>, 137–143. Würzburg, Germany, 2020. <a href=\"https://doi.org/10.1007/978-3-030-43823-4_12\">https://doi.org/10.1007/978-3-030-43823-4_12</a>.","mla":"Carnein, Matthias, et al. “Towards Automated Configuration of Stream Clustering Algorithms.” <i>Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)</i>, 2020, pp. 137–143, doi:<a href=\"https://doi.org/10.1007/978-3-030-43823-4_12\">10.1007/978-3-030-43823-4_12</a>."},"department":[{"_id":"34"},{"_id":"819"}],"doi":"10.1007/978-3-030-43823-4_12","date_created":"2023-08-04T07:35:24Z","_id":"46325","status":"public","author":[{"first_name":"Matthias","last_name":"Carnein","full_name":"Carnein, Matthias"},{"orcid":"0000-0002-9788-8282","first_name":"Heike","id":"100740","last_name":"Trautmann","full_name":"Trautmann, Heike"},{"last_name":"Bifet","full_name":"Bifet, Albert","first_name":"Albert"},{"first_name":"Bernhard","full_name":"Pfahringer, Bernhard","last_name":"Pfahringer"}],"year":"2020","publication_identifier":{"isbn":["978-3-030-43823-4"]},"user_id":"15504","title":"Towards Automated Configuration of Stream Clustering Algorithms","publication":"Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)","abstract":[{"text":"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.","lang":"eng"}],"language":[{"iso":"eng"}],"place":"Würzburg, Germany","page":"137–143","date_updated":"2023-10-16T13:03:15Z"}