Catálogo de publicaciones - libros
Título de Acceso Abierto
Projection-Based Clustering through Self-Organization and Swarm Intelligence: Projection-Based Clustering through Self-Organization and Swarm Intelligence
Resumen/Descripción – provisto por la editorial
No disponible.
Palabras clave – provistas por la editorial
Cluster Analysis; Dimensionality Reduction; Swarm Intelligence; Visualization; Unsupervised Machine Learning; Data Science; Knowledge Discovery; 3D Printing; Self-Organization; Emergence; Game Theory; Advanced Analytics; High-Dimensional Data; Multivariate Data; Analysis of Structured Data
Disponibilidad
| Institución detectada | Año de publicación | Navegá | Descargá | Solicitá |
|---|---|---|---|---|
| No requiere | 2018 | Directory of Open access Books |
| |
| No requiere | 2018 | SpringerLink |
|
Información
Tipo de recurso:
libros
ISBN impreso
978-3-658-20539-3
ISBN electrónico
978-3-658-20540-9
Editor responsable
Springer Nature
País de edición
Reino Unido
Fecha de publicación
2018
Tabla de contenidos
DBS on Natural Data Sets
Michael Christoph Thrun
Several real-world data sets are used in this chapter to show that Databionic swarm (DBS) is able to find clusters in a variety of cases. The leukemia data set is based on luminance measurements of 7747 different active or non-active genes in 554 human subjects. The World GDP data set is a multivariate time series that consists of monetary values for 190 countries from 1970 to 2010.
Pp. 129-136
Knowledge Discovery with DBS
Michael Christoph Thrun
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.
Pp. 137-148
Discussion
Michael Christoph Thrun
This work examined and analyzed patterns in high-dimensional data characterized by discontinuity. Such distance- or density-based patterns are either compact or connected structures. If the structures are compact, inter- versus intracluster distances are relevant.
Pp. 149-159
Conclusion
Michael Christoph Thrun
A new and data-driven approach for cluster analysis and visualization is introduced in this work. The projection based clustering combines structures preserved in two dimensions with underlying high-dimensional structures (see also [Thrun et al., 2017, Thrun/Ultsch, 2017a]). It is a flexible and robust approach for cluster analysis that consists of three independent modules which can be optionally combined into the Databionic swarm (DBS).
Pp. 161-162