Abstract
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 that 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. This article is an abstract of Swarm Intelligence for Self-Organized Clustering [1].
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Thrun, M.C., Ultsch, A. (2020). Swarm-Based Cluster Analysis for Knowledge Discovery. In: Schmid, U., Klügl, F., Wolter, D. (eds) KI 2020: Advances in Artificial Intelligence. KI 2020. Lecture Notes in Computer Science(), vol 12325. Springer, Cham. https://doi.org/10.1007/978-3-030-58285-2_18
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