Projection-Based Clustering through Self-Organization and Swarm Intelligence: Combining Cluster Analysis with the Visualization of High-Dimensional Data
Cluster Analysis; Dimensionality Reduction; Swarm Intelligence; Visualization; Unsupervised Machine Learning; Data Science; Knowledge Discovery; 3D Printing; Self-Organization; Emergence; Game Theory; Advanced Analytics; High-Dimensional Data; Multivariate Data; Analysis of Structured Data
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| Médium: | E-kniha |
| Jazyk: | angličtina |
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Cham
Springer Nature
2018
Springer Open Springer Gabler. in Springer Fachmedien Wiesbaden GmbH Springer Vieweg. in Springer Fachmedien Wiesbaden GmbH Springer |
| Vydání: | 1 |
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| ISBN: | 3658205407, 9783658205409, 3658205393, 9783658205393 |
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| Abstract | Cluster Analysis; Dimensionality Reduction; Swarm Intelligence; Visualization; Unsupervised Machine Learning; Data Science; Knowledge Discovery; 3D Printing; Self-Organization; Emergence; Game Theory; Advanced Analytics; High-Dimensional Data; Multivariate Data; Analysis of Structured Data |
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| AbstractList | Cluster Analysis; Dimensionality Reduction; Swarm Intelligence; Visualization; Unsupervised Machine Learning; Data Science; Knowledge Discovery; 3D Printing; Self-Organization; Emergence; Game Theory; Advanced Analytics; High-Dimensional Data; Multivariate Data; Analysis of Structured Data This open access 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. This open access 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. |
| Author | Christoph Thrun, Michael |
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| Snippet | Cluster Analysis; Dimensionality Reduction; Swarm Intelligence; Visualization; Unsupervised Machine Learning; Data Science; Knowledge Discovery; 3D Printing;... This open access book covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to... |
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| SubjectTerms | 3D Printing Advanced Analytics Analysis of Structured Data Cluster Analysis Computer Technology Data Science Dimensionality Reduction Emergence Game Theory High-Dimensional Data Knowledge Discovery Mathematics Mathematics and Science Multivariate Data Nonfiction Self-Organization Swarm Intelligence Unsupervised Machine Learning Visualization |
| SubjectTermsDisplay | Computer Technology. Electronic books. Nonfiction. |
| TableOfContents | 6.1.7 Mean Relative Rank Error (MRRE) and the Co-ranking Matrix -- 6.1.8 Precision and Recall -- 6.1.9 Rescaled Average Agreement Rate (RAAR) -- 6.1.10 Stress and the Shepard Diagram -- 6.1.11 Topographic Product -- 6.1.12 Topographic Function (TF) -- 6.1.13 Trustworthiness and Discontinuity (T& -- D) -- 6.1.14 U-ranking -- 6.1.15 Overall Correlations: Topological Index (TI) and Topological Correlation (TC) -- 6.1.16 Zrehen's Measure -- 6.2 Types of Quality Measures for Assessing Structure Preservation -- 6.2.1 Theoretical Assessment of Quality Measures -- 6.2.2 Practical Assessment of Quality Measures -- 6.3 Introducing the Delaunay Classification Error (DCE) -- 6.3.1 Summary -- 7 Behavior-based Systems in Data Science -- 7.1 Artificial Behavior Based on DataBots -- 7.1.1 Swarm-Organized Projection (SOP) -- 7.2 Swarm Intelligence for Unsupervised Machine Learning -- 7.3 Missing Links: Emergence and Game Theory -- 8 Databionic Swarm (DBS) -- 8.1 Projection with Pswarm -- 8.1.1 Motivation: Game Theory -- 8.1.2 Symmetry Considerations -- 8.1.3 Algorithm -- 8.1.4 Data-driven Annealing Scheme -- 8.1.5 Annealing Interval -- 8.1.6 Convergence -- 8.2 Comparing Pswarm with a Previously Developed Approach -- 8.2.1 Neighborhood Definition -- 8.2.2 Annealing Scheme -- 8.2.3 Swarm Intelligence and Self-Organization -- 8.3 Clustering on a Generalized U*-Matrix -- 9 Experimental Methodology -- 9.1 Data Sets -- 9.1.1 Atom -- 9.1.2 Chainlink -- 9.1.3 EngyTime -- 9.1.4 Golf Ball -- 9.1.5 Hepta -- 9.1.6 Iris -- 9.1.7 Leukemia -- 9.1.8 Lsun3D -- 9.1.9 S-shape -- 9.1.10 Swiss Banknotes -- 9.1.11 Target -- 9.1.12 Tetra -- 9.1.13 Tetragonula -- 9.1.14 Cuboid -- 9.1.15 Two Diamonds -- 9.1.16 Wine -- 9.1.17 Wing Nut -- 9.1.18 World Gross Domestic Product (World GDP) -- 9.2 Parameter Settings -- 9.2.1 Quality Measures (QMs) -- 9.2.2 Projection Methods Intro -- Acknowledgments -- Table of contents -- List of figures -- List of tables -- Zusammenfassung -- Abstract -- 1 Introduction -- 2 Fundamentals -- 2.1 Basic Definitions -- 2.2 Concepts of Graph Theory Applied to Patterns -- 2.3 Overview of Knowledge Discovery -- 2.3.1 Feature Selection -- 2.3.2 Preprocessing -- 2.3.3 Feature Extraction -- 2.3.3.1 Transformations -- 2.3.3.2 Dimensionality Reduction -- 2.3.4 Cluster Analysis -- 2.3.5 An Approach to Knowledge Acquisition -- 3 Approaches to Cluster Analysis -- 3.1 Common Clustering Methods -- 3.2 Structure of Natural Clusters -- 3.2.1 Types of Structures Sought by Clustering Algorithms -- 3.2.2 Quality of Clustering -- 3.2.2.1 Heatmaps -- 3.2.2.2 Silhouette plots -- 3.3 Problems with Clustering Methods -- 4 Methods of Projection -- 4.1 Common Approaches -- 4.1.1 Principal Component Analysis (PCA) -- 4.1.2 Independent Component Analysis (ICA) -- 4.1.3 Non-linear metric multidimensional scaling (MDS) techniques -- 4.1.4 Curvilinear Component Analysis (CCA) -- 4.1.5 t-Distributed Stochastic Neighbor Embedding (t-SNE) -- 4.1.6 Neighborhood Retrieval Visualizer (NeRV) -- 4.2 Emergent Self-Organizing Map (ESOM) -- 4.2.1 Visualizations of SOMs -- 4.2.2 Clustering with ESOM -- 4.3 Types of Projection Methods -- 5 Visualizing the Output Space -- 5.1 Examples -- 5.2 Structure Preservation -- 5.3 Generating a Topographic Map from the Generalized U*-matrix -- 5.3.1 Simplified ESOM -- 5.3.2 U*-Matrix Calculation -- 5.3.3 Topographic Map with Hypsometric Tints -- 5.3.4 Limitations -- 6 Quality Assessments of Visualizations -- 6.1 Common Quality Measures (QMs) -- 6.1.1 Classification Error (CE) -- 6.1.2 C Measure -- 6.1.3 Two Variants of the C Measure: Minimal Path Length and Minimal Wiring -- 6.1.4 Force Approach Error -- 6.1.5 König's Measure -- 6.1.6 Local Continuity Meta-Criterion (LCMC) 9.2.2.1 Swarm-Organized Projection (SOP) -- 9.2.2.2 Pswarm -- 9.2.3 Common clustering algorithms -- 9.3 Gene Ontology (GO) -- 9.3.1 Overrepresentation Analysis (ORA) -- 9.3.2 Filtering via ABC Analysis -- 10 Results on Pre-classified Data Sets -- 10.1 Comparison with Given Classifications -- 10.1.1 Recognition of the Absence of Clusters -- 10.2 Evaluation of Projections Using the Delaunay Classification Error (DCE) -- 10.3 Topographic Maps with Hypsometric Colors -- 11 DBS on Natural Data Sets -- 11.1 Types of Leukemia -- 11.2 World Gross Domestic Product (World GDP) -- 11.3 Tetragonula Bees -- 12 Knowledge Discovery with DBS -- 12.1 Hydrology -- 12.1.1 Knowledge Acquisition and Prediction in the Hydrology Data Set -- 12.2 Pain Genes -- 12.2.1 Prior Knowledge -- 12.2.2 Knowledge Acquisition in Clusters of Pain Genes -- 13 Discussion -- 14 Conclusion -- References -- Appendices -- Supplement A: Evaluation of Common QMs -- Supplement B: Wine Dataset Distance Distribution -- Supplement C: Generalized Umatrix of Pswarm and SOP -- Supplement D: DBS Visualizations of S-shape and uniform Cuboid -- Supplement E: U-Matrix Visualizations of ESOM Projections -- Supplement F: Statistical Tests in Hydrology -- Supplement G: 3D Prints of Generalized Umatrix Visualizations of DBS -- Supplement H: Contingency Table for Tetragonula Bees Clustering -- Index |
| Title | Projection-Based Clustering through Self-Organization and Swarm Intelligence: Combining Cluster Analysis with the Visualization of High-Dimensional Data |
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