Projection-Based Clustering through Self-Organization and Swarm Intelligence Combining Cluster Analysis with the Visualization of High-Dimensional Data /
This book is published open access under a CC BY 4.0 license. It covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cluster analysis, the Databionic swarm(DBS). DBS consists of the 3D landscape visualization and clust...
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
Wiesbaden :
Springer Fachmedien Wiesbaden : Imprint: Springer Vieweg,
2018.
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| Edition: | 1st ed. 2018. |
| Subjects: | |
| Online Access: | Link to Metadata |
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| Summary: | This book is published open access under a CC BY 4.0 license. It covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cluster analysis, the Databionic swarm(DBS). DBS consists of the 3D landscape visualization and clustering of data. The 3D landscape enables 3D printing of high-dimensional data structures.The clustering and number of clusters or an absence of cluster structure are verified by the 3D landscape at a glance. DBS is the first swarm-based technique that shows emergent properties while exploiting concepts of swarm intelligence, self-organization and the Nash equilibrium concept from game theory. It results in the elimination of a global objective function and the setting of parameters. By downloading the R package DBS can be applied to data drawn from diverse research fields and used even by non-professionals in the field of data mining. Contents Approaches to Unsupervised Machine Learning Methods of Visualization of High-Dimensional Data Quality Assessments of Visualizations Behavior-Based Systems in Data Science Databionic Swarm (DBS) Target Groups Lecturers, students as well as non-professional users of data science, statistics, computer science, business mathematics, medicine, biology The Author Michael C. Thrun, Dipl.-Phys., successfully defended his Ph.D. in 2017 at the Philipps University of Marburg. Thrun's advisor was the Chair of Neuroinformatics, Prof. Dr. rer. nat. Alfred G. H. Ultsch. |
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| Physical Description: | XX, 201 p. 90 illus., 29 illus. in color. online resource. |
| ISBN: | 9783658205409 |
| DOI: | 10.1007/978-3-658-20540-9 |
| Access: | Open Access |