Abstract
In contrast to chapter 11, in which Databionic swarm (DBS) clustering was applied to recognize more or less obvious knowledge, this chapter shows that DBS is also able to discover new knowledge. A hydrological data set of multivariate time series [Aubert et al., 2016] and a data set consisting of pain genes [Ultsch et al., 2016b] are used for this purpose. In [Aubert et al., 2016], a high-frequency time series analysis was performed, but no prediction could be made.
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Thrun, M.C. (2018). Knowledge Discovery with DBS. In: Projection-Based Clustering through Self-Organization and Swarm Intelligence. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-20540-9_12
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DOI: https://doi.org/10.1007/978-3-658-20540-9_12
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Publisher Name: Springer Vieweg, Wiesbaden
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Online ISBN: 978-3-658-20540-9
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