Projection-Based Clustering through Self-Organization and Swarm Intelligence [[electronic resource] ] : Combining Cluster Analysis with the Visualization of High-Dimensional Data / / by Michael Christoph Thrun

• Autore: Thrun Michael Christoph
• Pubblicazione: Cham, : Springer Nature, 2018; Wiesbaden : , : Springer Fachmedien Wiesbaden : , : Imprint : Springer Vieweg, , 2018
• Edizione: 1st ed. 2018.
• Descrizione fisica: 1 online resource (XX, 201 p. 90 illus., 29 illus. in color.)
• Disciplina: 006.4
• Soggetto topico: Pattern recognition; Data structures (Computer science); Pattern Recognition; Data Structures
• Soggetto non controllato: 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
• Nota di contenuto: 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).
• Sommario/riassunto: 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.
• Titolo autorizzato: Projection-Based Clustering through Self-Organization and Swarm Intelligence
• ISBN: 3-658-20540-7
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910293141703321
• Lo trovi qui: Univ. Federico II