Title
Visual Knowledge Discovery and Machine Learning
Document Type
Book
Department or Administrative Unit
Computer Science
Publication Date
2018
Abstract
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.
Recommended Citation
Kovalerchuk, B. (2018). Visual Knowledge Discovery and Machine Learning. Springer. https://doi.org/10.1007/978-3-319-73040-0
Rights
© Springer International Publishing AG 2018
Comments
This book was originally published by Springer. The full-text article from the publisher can be found here.
Due to copyright restrictions, this article is not available for free download from ScholarWorks @ CWU.