OnlineAutoClust: A Framework for Online Automated Clustering
Radwa El Shawi, Dmitri Rozgonjuk · 2023
Automated Machine Learning (AutoML) has been successful when the learning task is assumed to be static. However, it remains unclear whether AutoML methods can efficiently create online pipelines in dynamic environments. The current online AutoML frameworks primarily focus on supervised learning. However, unsupervised learning, particularly clustering, also requires AutoML solutions, especially with the ambiguity associated with evaluating clustering results. In this paper, we introduce OnlineAutoClust, a framework for online automated clustering for algorithm selection and hyperparameter tuning. OnlineAutoClust combines the inherent adaptation capabilities of online learners with automated pipeline optimization using Bayesian optimization. OnlineAutoClust develops a collaborative mechanism based on clustering ensemble to combine optimized pipelines based on different internal cluster validity indices. The proposed framework is based on River library and utilizes five clustering algorithms. Empirical evaluation on several real and synthetic data streams with varying types of concept drift demonstrates the effectiveness of the proposed approach compared to existing methods