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020 _a9783658205409
_9978-3-658-20540-9
024 7 _a10.1007/978-3-658-20540-9
_2doi
072 7 _aUYQP
_2bicssc
072 7 _aCOM016000
_2bisacsh
072 7 _aUYQP
_2thema
082 0 4 _a006.4
_223
100 1 _aThrun, Michael Christoph.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aProjection-Based Clustering through Self-Organization and Swarm Intelligence
_h[electronic resource] :
_bCombining Cluster Analysis with the Visualization of High-Dimensional Data /
_cby Michael Christoph Thrun.
250 _a1st ed. 2018.
264 1 _aWiesbaden :
_bSpringer Fachmedien Wiesbaden :
_bImprint: Springer Vieweg,
_c2018.
300 _aXX, 201 páginas90 ilustraciones, 29 ilustraciones in color.
_bonline resource.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aApproaches to Unsupervised Machine Learning -- Methods of Visualization of High-Dimensional Data -- Quality Assessments of Visualizations -- Behavior-Based Systems in Data Science -- Databionic Swarm (DBS).
506 0 _aOpen Access
520 _aThis 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.
650 0 _aPattern recognition systems.
650 0 _aArtificial intelligence-Data processing.
650 1 4 _aAutomated Pattern Recognition.
650 2 4 _aData Science.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783658205393
776 0 8 _iPrinted edition:
_z9783658205416
856 4 0 _uhttps://doi.org/10.1007/978-3-658-20540-9
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-SOB
999 _c154648
_d154648