| Description |
1 online resource (xx, 201 pages) : illustrations (some color) |
| Access |
Open access. GW5XE |
| Bibliography |
Includes bibliographical references and index. |
| Note |
Online resource; title from PDF title page (SpringerLink, viewed January 18, 2018). |
| 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). |
| 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. |
| Local Note |
SpringerLink Springer Nature Open Access eBooks |
| Subject |
Swarm intelligence.
|
|
Self-organizing systems.
|
|
Mathematics & science.
|
|
Self-organizing systems. (OCoLC)fst01111791
|
|
Swarm intelligence. (OCoLC)fst01139953
|
| ISBN |
9783658205409 (electronic book) |
|
3658205407 (electronic book) |
|
9783658205393 (print) |
| Standard No. |
10.1007/978-3-658-20540-9 doi |
|
