Swarm Intelligence for Self-Organized Clustering (Extended Abstract)
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Journal track. Pages 5125-5129. https://doi.org/10.24963/ijcai.2020/720
The Databionic swarm (DBS) is a flexible and robust clustering framework that consists of three independent modules: swarm based projection, high-dimensional data visualization and representation guided clustering. The first module is the parameter-free projection method Pswarm, which exploits concepts of self-organization and emergence, game theory, and swarm intelligence. The second module is a parameter-free high-dimensional data visualization technique called topographic map. It uses the generalized U-matrix, which enables to estimate first, if any cluster tendency exists and second, the estimation of the number of clusters. The third module offers a clustering method which can be verified by the visualization and vice versa. Benchmarking w.r.t. conventional algorithms demonstrated that DBS can outperform them. Several applications showed that cluster structures provided by DBS are meaningful. Exemplary, a clustering of worldwide country-related data w.r.t the COVID-19 pandemic is presented here. Code and data is made available via open source.
Agent-based and Multi-agent Systems: Multi-agent Learning
Agent-based and Multi-agent Systems: Noncooperative Games
Computer Vision: Statistical Methods and Machine Learning
Data Mining: Clustering, Unsupervised Learning