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 LEADER 03939nam 22006015 450 001 9910337515903321 005 20251116212050.0 010 $a3-030-11515-1 024 7 $a10.1007/978-3-030-11515-9 035 $a(CKB)4100000007816624 035 $a(MiAaPQ)EBC5739916 035 $a(DE-He213)978-3-030-11515-9 035 $a(PPN)235233153 035 $a(EXLCZ)994100000007816624 100 $a20190323d2019 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aLymph Node Pathology for Clinicians /$fby Michel R. Nasr, Anamarija M. Perry, Pamela Skrabek 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (217 pages) 225 1 $aPathology for Clinicians,$x2570-3382 311 08$a3-030-11514-3 327 $aIntroduction to Lymph Node Pathology -- Diagnostic Methods -- The Normal Lymph Node -- Benign Lymph Node Conditions -- Nomenclature and Classification of Lymphomas -- Precursor Lymphoid Neoplasms -- Mature B-cell Neoplasms -- Mature T- and NK-cell Neoplasms -- Hodgkin Lymphoma -- Immunodeficiency-associated Lymphoproliferative Disorders -- Granulocytic, Histiocytic and Dendritic Cell Neoplasms. 330 $aLymph node pathology is a complex and rapidly evolving field that requires integration of morphologic findings with a number of ancillary studies, as well as clinical information, to diagnose neoplastic and non-neoplastic hematopoietic disorders. Lymphomas are currently classified according to the 2016 revision of the World Health Organization (WHO) Classification, which emphasizes, and for some diagnoses mandates, the integration of clinical information in diagnostic decision making. Successful collaboration and teamwork between pathology and clinical specialties, especially hematology/oncology, are paramount for excellent patient care. In addition to diagnosis, pathology plays a significant role in lymphoma prognostication and contributes to patient management and follow-up. Lymph Node Pathology for Clinicians provides a concise overview of different entities in lymph node pathology with the primary audience being clinicians. This text is intended as a quick reference for a clinician to become familiar with pathologic aspects of lymphomas and the thought process of a pathologist. Particular consideration is given to relevant diagnostic and prognostic ancillary studies. Organized with an interdisciplinary approach for effective management of lymph node disorders, this book aims to educate our clinical colleagues on the most important aspects of lymph node pathology. 410 0$aPathology for Clinicians,$x2570-3382 606 $aPathology 606 $aCancer$xSurgery 606 $aOncology 606 $aSurgery 606 $aPathology$3https://scigraph.springernature.com/ontologies/product-market-codes/H4800X 606 $aSurgical Oncology$3https://scigraph.springernature.com/ontologies/product-market-codes/H59150 606 $aOncology$3https://scigraph.springernature.com/ontologies/product-market-codes/H33160 606 $aGeneral Surgery$3https://scigraph.springernature.com/ontologies/product-market-codes/H59044 615 0$aPathology. 615 0$aCancer$xSurgery. 615 0$aOncology. 615 0$aSurgery. 615 14$aPathology. 615 24$aSurgical Oncology. 615 24$aOncology. 615 24$aGeneral Surgery. 676 $a616.42 700 $aNasr$b Michel R.$4aut$4http://id.loc.gov/vocabulary/relators/aut$0782504 702 $aPerry$b Anamarija M.$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aSkrabek$b Pamela$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910337515903321 996 $aLymph Node Pathology for Clinicians$92505621 997 $aUNINA