This book combines the advantages of high-dimensional data visualization and machine learning in the context of identifying complex n-D data patterns. It vastly expands the class of reversible lossless 2-D and 3-D visualization methods, which preserve the n-D information. This class of visual representations, called the General Lines Coordinates (GLCs), is accompanied by a set of algorithms for n-D data classification, clustering, dimension reduction, and Pareto optimization. The mathematical and theoretical analyses and methodology of GLC are included, and the usefulness of this new approach is demonstrated in multiple case studies. These include the Challenger disaster, world hunger data, health monitoring, image processing, text classification, market forecasts for a currency exchange rate, computer-aided medical diagnostics, and others. As such, the book offers a unique resource for students, researchers, and practitioners in the emerging field of Data Science.
Chapter 1 Motivation, Problems And Approach
Chapter 2 General Line Coordinates (Glc)
Chapter 3 Theoretical And Mathematical Basis Of Glc
Chapter 4 Adjustable Glcs For Decreasing Occlusion And Pattern Simplification
Chapter 5 Glc Case Studies
Chapter 6 Discovering Visual Features And Shape Perception Capabilities In Glc
Chapter 7 Interactive Visual Classification, Clustering And Dimension Reduction With Glc-L
Chapter 8 Knowledge Discovery And Machine Learning For Investment Strategy With Cpc
Chapter 9 Visual Text Mining: Discovery Of Incongruity In Humor Modeling
Chapter 10 Enhancing Evaluation Of Machine Learning Algorithms With Visual Means
Chapter 11 Pareto Front And General Line Coordinates
Chapter 12 Toward Virtual Data Scientist And Super-Intelligence With Visual Means
Chapter 13 Comparison And Fusion Of Methods And Future Research
