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dc.contributor.authorMichael Christoph Thrun*
dc.date.accessioned2021-02-12T00:09:57Z
dc.date.available2021-02-12T00:09:57Z
dc.date.issued2018*
dc.date.submitted2018-06-29 15:12:23*
dc.identifier27229*
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/57169
dc.description.abstractThis book 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.*
dc.languageEnglish*
dc.subjectQA1-939*
dc.subject.otherVisualization*
dc.subject.otherKnowledge Discovery*
dc.subject.otherSwarm Intelligence*
dc.subject.otherUnsupervised Machine Learning*
dc.subject.otherData Science*
dc.subject.otherGame Theory*
dc.subject.other3D Printing*
dc.subject.otherDimensionality Reduction*
dc.subject.otherMultivariate Data*
dc.subject.otherAnalysis of Structured Data*
dc.subject.otherSelf-Organization*
dc.subject.otherEmergence*
dc.subject.otherAdvanced Analytics*
dc.subject.otherHigh-Dimensional Data*
dc.subject.otherCluster Analysis*
dc.titleProjection-Based Clustering through Self-Organization and Swarm Intelligence: Combining Cluster Analysis with the Visualization of High-Dimensional Data*
dc.typebook
oapen.identifier.doihttps://doi.org/10.1007/978-3-658-20540-9*
oapen.relation.isPublishedBy9fa3421d-f917-4153-b9ab-fc337c396b5a*
oapen.relation.isFundedByacd215e4-f8b7-4c00-9473-b73f0ff2a0d0*
oapen.relation.isbn9783658205393*
oapen.relation.isbn9783658205409*
oapen.pages201*
oapen.grant.numberUniversität Marburg


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