Verfügbarkeit: Projection-based clustering through self-organization and swarm intelligence

Projection-based clustering through self-organization and swarm intelligence: combining cluster analysis with the visualization of high-dimensional data

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Bibliographische Detailangaben
1. Verfasser:	Thrun, Michael Christoph (VerfasserIn)
Format:	Abschlussarbeit Buch
Sprache:	English
Veröffentlicht:	Wiesbaden Springer Vieweg [2018]
Schlagworte:	Dimensionsreduktion Visualisierung Wissensextraktion Projektionsverfahren Schwarmintelligenz Cluster-Analyse Hochdimensionale Daten Selbstorganisation Unüberwachtes Lernen Open Access Cluster Analysis Dimensionality Reduction Swarm Intelligence Visualization Unsupervised machine learning Data science Knowledge Discovery Self-Organization Emergence Game theory Advanced Analytics High-dimensional data Multivariate data Analysis of stuctured data Hochschulschrift
Online-Zugang:	Volltext Inhaltsverzeichnis
Beschreibung:	XX, 201 Seiten Illustrationen, Diagramme 24 cm x 16.8 cm
ISBN:	9783658205393 3658205393
DOI:	10.1007/978-3-658-20540-9

Internformat

MARC


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100	1		\|a Thrun, Michael Christoph \|e Verfasser \|0 (DE-588)1155946553 \|4 aut
240	1	0	\|a A system for projection-based clustering through self-organization and swarm intelligence
245	1	0	\|a Projection-based clustering through self-organization and swarm intelligence \|b combining cluster analysis with the visualization of high-dimensional data \|c Michael Christoph Thrun
264		1	\|a Wiesbaden \|b Springer Vieweg \|c [2018]
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300			\|a XX, 201 Seiten \|b Illustrationen, Diagramme \|c 24 cm x 16.8 cm
336			\|b txt \|2 rdacontent
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502			\|b Dissertation \|c Philipps-Universität Marburg \|d 2017
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653			\|a Knowledge Discovery
653			\|a Self-Organization
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653			\|a Game theory
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Datensatz im Suchindex

DE-BY-FWS_katkey	712825
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adam_text	TABLE OF CONTENTS DANKSAGUNG............................................................................................................................... V LIST OF FIGURES........................................................................................................................... IX LIST OF TABLES............................................................................................................................XV ZUSAMMENFASSUNG.............................................................................................................. XVII ABSTRACT................................................................................................................................. XIX 1 INTRODUCTION.........................................................................................................................1 2 FUNDAMENTALS..................................................................................................................... 5 2.1 BASIC DEFINITIONS........................................................................................................ 5 2.2 CONCEPTS OF GRAPH THEORY APPLIED TO PATTERNS........................................................ 10 2.3 OVERVIEW OF KNOWLEDGE DISCOVERY......................................................................... 15 3 APPROACHES TO CLUSTER ANALYSIS......................................................................................21 3.1 COMMON CLUSTERING METHODS....................................................................................22 3.2 STRUCTURE OF NATURAL CLUSTERS....................................................................................26 3.3 PROBLEMS WITH CLUSTERING METHODS..........................................................................29 4 METHODS OF PROJECTION...................................................................................................... 33 4.1 COMMON APPROACHES...............................................................................................33 4.2 EMERGENT SELF-ORGANIZING MAP (ESOM).................................................................37 4.3 TYPES OF PROJECTION METHODS....................................................................................40 5 VISUALIZING THE OUTPUT SPACE.......................................................................................... 43 5.1 EXAMPLES...................................................................................................................43 5.2 STRUCTURE PRESERVATION................................................................................................45 5.3 GENERATING A TOPOGRAPHIC MAP FROM THE GENERALIZED U-MATRIX .......................... 46 6 QUALITY ASSESSMENTS OF VISUALIZATIONS...........................................................................55 6.1 COMMON QUALITY MEASURES (QMS)..........................................................................58 6.2 TYPES OF QUALITY MEASURES FOR ASSESSING STRUCTURE PRESERVATION ........................... 67 6.3 INTRODUCING THE DELAUNAY CLASSIFICATION ERROR (DCE).............................................73 7 BEHAVIOR-BASED SYSTEMS IN DATA SCIENCE.......................................................................77 7.1 ARTIFICIAL BEHAVIOR BASED ON DATABOTS .................................................................... 80 7.2 SWARM INTELLIGENCE FOR UNSUPERVISED MACHINE LEARNING........................................83 7.3 MISSING LINKS: EMERGENCE AND GAME THEORY.........................................................87 8 DATABIONIC SWARM (DBS)................................................................................................91 8.1 PROJ ECTION WITH PSWARM............................................................................................91 8.2 COMPARING PSWARM WITH A PREVIOUSLY DEVELOPED APPROACH ................................. 98 8.3 CLUSTERING ON A GENERALIZED U-MATRIX.................................................................104 9 EXPERIMENTAL METHODOLOGY........................................................................................... 107 9.1 DATASETS................................................................................................................. 107 9.2 PARAMETER SETTINGS.................................................................................................. I L L 9.3 GENE ONTOLOGY (GO).............................................................................................. 113 10 RESULTS ON PRE-CLASSIFIED DATA SETS............................................................................... 117 10.1 COMPARISON WITH GIVEN CLASSIFICATIONS................................................................. 117 10.2 EVALUATION OF PROJECTIONS USING THE DELAUNAY CLASSIFICATION ERROR (DCE) ............ 120 10.3 TOPOGRAPHIC MAPS WITH HYPSOMETRIC COLORS ......................................................... 122 11 DBS ON NATURAL DATA SETS............................................................................................. 129 11.1 TYPES OF LEUKEMIA................................................................................................. 129 11.2 WORLD GROSS DOMESTIC PRODUCT (WORLD GDP).......................................................129 11.3 TETRAGONULA BEES................................................................................................... 132 12 KNOWLEDGE DISCOVERY WITH DBS ................................................................................... 137 12.1 HYDROLOGY............................................................................................................... 137 12.2 PAIN GENES...............................................................................................................143 13 DISCUSSION.........................................................................................................................149 14 CONCLUSION........................................................................................................................161 REFERENCES.............................................................................................................................. 163 APPENDICES............................................................................................................................. 179 SUPPLEMENT A: EVALUATION OF COMMON QMS....................................................179 SUPPLEMENT B: WINE DATASET DISTANCE DISTRIBUTION..........................................185 SUPPLEMENT C: GENERALIZED UMATRIX OF PSWARM AND SOP ................................ 186 SUPPLEMENT D: DBS VISUALIZATIONS OF S-SHAPE AND UNIFORM CUBOID ............... 191 SUPPLEMENT E: U-MATRIX VISUALIZATIONS OF ESOM PROJECTIONS ........................ 192 SUPPLEMENT F: STATISTICAL TESTS IN HYDROLOGY ................................................... 194 SUPPLEMENT G: 3D PRINTS OF GENERALIZED UMATRIX VISUALIZATIONS OF DBS ....... 195 SUPPLEMENT H: CONTINGENCY TABLE FOR TETRAGONULA BEES CLUSTERING ................ 196 SUPPLEMENT I: STATISTICAL TESTS FOR FCPS CLUSTERING COMPARED TO DBS ........... 197 INDEX........................................................................................................................................199
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author	Thrun, Michael Christoph
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dewey-hundreds	000 - Computer science, information, general works
dewey-ones	004 - Computer science
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discipline	Informatik
doi_str_mv	10.1007/978-3-658-20540-9
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owner_facet	DE-83 DE-525 DE-12 DE-634 DE-210 DE-521 DE-1102 DE-Aug4 DE-573 DE-M347 DE-92 DE-1051 DE-898 DE-BY-UBR DE-859 DE-860 DE-1049 DE-863 DE-BY-FWS DE-862 DE-BY-FWS DE-523 DE-Y3 DE-255 DE-Y7 DE-Y2 DE-70 DE-2174 DE-127 DE-22 DE-BY-UBG DE-155 DE-BY-UBR DE-150 DE-91 DE-BY-TUM DE-384 DE-473 DE-BY-UBG DE-19 DE-BY-UBM DE-355 DE-BY-UBR DE-703 DE-20 DE-706 DE-824 DE-29 DE-739 DE-235
physical	XX, 201 Seiten Illustrationen, Diagramme 24 cm x 16.8 cm
psigel	ebook
publishDate	2018
publishDateSearch	2018
publishDateSort	2018
publisher	Springer Vieweg
record_format	marc
spellingShingle	Thrun, Michael Christoph Projection-based clustering through self-organization and swarm intelligence combining cluster analysis with the visualization of high-dimensional data Dimensionsreduktion (DE-588)4224279-4 gnd Visualisierung (DE-588)4188417-6 gnd Wissensextraktion (DE-588)4546354-2 gnd Projektionsverfahren (DE-588)4175879-1 gnd Schwarmintelligenz (DE-588)4793676-9 gnd Cluster-Analyse (DE-588)4070044-6 gnd Hochdimensionale Daten (DE-588)7862975-5 gnd Selbstorganisation (DE-588)4126830-1 gnd Unüberwachtes Lernen (DE-588)4580265-8 gnd
subject_GND	(DE-588)4224279-4 (DE-588)4188417-6 (DE-588)4546354-2 (DE-588)4175879-1 (DE-588)4793676-9 (DE-588)4070044-6 (DE-588)7862975-5 (DE-588)4126830-1 (DE-588)4580265-8 (DE-588)4113937-9
title	Projection-based clustering through self-organization and swarm intelligence combining cluster analysis with the visualization of high-dimensional data
title_alt	A system for projection-based clustering through self-organization and swarm intelligence
title_auth	Projection-based clustering through self-organization and swarm intelligence combining cluster analysis with the visualization of high-dimensional data
title_exact_search	Projection-based clustering through self-organization and swarm intelligence combining cluster analysis with the visualization of high-dimensional data
title_full	Projection-based clustering through self-organization and swarm intelligence combining cluster analysis with the visualization of high-dimensional data Michael Christoph Thrun
title_fullStr	Projection-based clustering through self-organization and swarm intelligence combining cluster analysis with the visualization of high-dimensional data Michael Christoph Thrun
title_full_unstemmed	Projection-based clustering through self-organization and swarm intelligence combining cluster analysis with the visualization of high-dimensional data Michael Christoph Thrun
title_short	Projection-based clustering through self-organization and swarm intelligence
title_sort	projection based clustering through self organization and swarm intelligence combining cluster analysis with the visualization of high dimensional data
title_sub	combining cluster analysis with the visualization of high-dimensional data
topic	Dimensionsreduktion (DE-588)4224279-4 gnd Visualisierung (DE-588)4188417-6 gnd Wissensextraktion (DE-588)4546354-2 gnd Projektionsverfahren (DE-588)4175879-1 gnd Schwarmintelligenz (DE-588)4793676-9 gnd Cluster-Analyse (DE-588)4070044-6 gnd Hochdimensionale Daten (DE-588)7862975-5 gnd Selbstorganisation (DE-588)4126830-1 gnd Unüberwachtes Lernen (DE-588)4580265-8 gnd
topic_facet	Dimensionsreduktion Visualisierung Wissensextraktion Projektionsverfahren Schwarmintelligenz Cluster-Analyse Hochdimensionale Daten Selbstorganisation Unüberwachtes Lernen Hochschulschrift
url	https://doi.org/10.1007/978-3-658-20540-9 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030125284&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT thrunmichaelchristoph asystemforprojectionbasedclusteringthroughselforganizationandswarmintelligence AT thrunmichaelchristoph projectionbasedclusteringthroughselforganizationandswarmintelligencecombiningclusteranalysiswiththevisualizationofhighdimensionaldata

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