LEADER 04399 am 22007573u 450 001 9910293141703321 005 20230125183347.0 010 $a3-658-20540-7 024 7 $a10.1007/978-3-658-20540-9 035 $a(CKB)4100000001795004 035 $a(DE-He213)978-3-658-20540-9 035 $a(MiAaPQ)EBC5599496 035 $a(Au-PeEL)EBL5599496 035 $a(OCoLC)1076259214 035 $a(MiAaPQ)EBC6422736 035 $a(Au-PeEL)EBL6422736 035 $a(OCoLC)1231610904 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/34375 035 $a(PPN)223955019 035 $a(EXLCZ)994100000001795004 100 $a20180109d2018 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aProjection-Based Clustering through Self-Organization and Swarm Intelligence$b[electronic resource] $eCombining Cluster Analysis with the Visualization of High-Dimensional Data /$fby Michael Christoph Thrun 205 $a1st ed. 2018. 210 $aCham$cSpringer Nature$d2018 210 1$aWiesbaden :$cSpringer Fachmedien Wiesbaden :$cImprint: Springer Vieweg,$d2018. 215 $a1 online resource (XX, 201 p. 90 illus., 29 illus. in color.) 311 $a3-658-20539-3 327 $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). 330 $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. 606 $aPattern recognition 606 $aData structures (Computer science) 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aData Structures$3https://scigraph.springernature.com/ontologies/product-market-codes/I15017 610 $aCluster Analysis 610 $aDimensionality Reduction 610 $aSwarm Intelligence 610 $aVisualization 610 $aUnsupervised Machine Learning 610 $aData Science 610 $aKnowledge Discovery 610 $a3D Printing 610 $aSelf-Organization 610 $aEmergence 610 $aGame Theory 610 $aAdvanced Analytics 610 $aHigh-Dimensional Data 610 $aMultivariate Data 610 $aAnalysis of Structured Data 615 0$aPattern recognition. 615 0$aData structures (Computer science). 615 14$aPattern Recognition. 615 24$aData Structures. 676 $a006.4 700 $aThrun$b Michael Christoph$4aut$4http://id.loc.gov/vocabulary/relators/aut$0909471 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910293141703321 996 $aProjection-Based Clustering through Self-Organization and Swarm Intelligence$92035037 997 $aUNINA