TY - GEN
T1 - Towards automated configuration of stream clustering algorithms
AU - Carnein, Matthias
AU - Trautmann, Heike
AU - Bifet, Albert
AU - Pfahringer, Bernhard
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - 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.
AB - 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.
KW - Algorithm selection
KW - Automated algorithm configuration
KW - Ensemble techniques
KW - Stream clustering
U2 - 10.1007/978-3-030-43823-4_12
DO - 10.1007/978-3-030-43823-4_12
M3 - Conference contribution
AN - SCOPUS:85083745774
SN - 9783030438227
T3 - Communications in Computer and Information Science
SP - 137
EP - 143
BT - Machine Learning and Knowledge Discovery in Databases - International Workshops of ECML PKDD 2019, Proceedings
A2 - Cellier, Peggy
A2 - Driessens, Kurt
PB - Springer
T2 - 19th Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019
Y2 - 16 September 2019 through 20 September 2019
ER -