04399 am 22007573u 450 991029314170332120230125183347.03-658-20540-710.1007/978-3-658-20540-9(CKB)4100000001795004(DE-He213)978-3-658-20540-9(MiAaPQ)EBC5599496(Au-PeEL)EBL5599496(OCoLC)1076259214(MiAaPQ)EBC6422736(Au-PeEL)EBL6422736(OCoLC)1231610904(oapen)https://directory.doabooks.org/handle/20.500.12854/34375(PPN)223955019(EXLCZ)99410000000179500420180109d2018 u| 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierProjection-Based Clustering through Self-Organization and Swarm Intelligence[electronic resource] Combining Cluster Analysis with the Visualization of High-Dimensional Data /by Michael Christoph Thrun1st ed. 2018.ChamSpringer Nature2018Wiesbaden :Springer Fachmedien Wiesbaden :Imprint: Springer Vieweg,2018.1 online resource (XX, 201 p. 90 illus., 29 illus. in color.)3-658-20539-3 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).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.Pattern recognitionData structures (Computer science)Pattern Recognitionhttps://scigraph.springernature.com/ontologies/product-market-codes/I2203XData Structureshttps://scigraph.springernature.com/ontologies/product-market-codes/I15017Cluster AnalysisDimensionality ReductionSwarm IntelligenceVisualizationUnsupervised Machine LearningData ScienceKnowledge Discovery3D PrintingSelf-OrganizationEmergenceGame TheoryAdvanced AnalyticsHigh-Dimensional DataMultivariate DataAnalysis of Structured DataPattern recognition.Data structures (Computer science).Pattern Recognition.Data Structures.006.4Thrun Michael Christophauthttp://id.loc.gov/vocabulary/relators/aut909471MiAaPQMiAaPQMiAaPQBOOK9910293141703321Projection-Based Clustering through Self-Organization and Swarm Intelligence2035037UNINA