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Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery

  • Book
  • © 2024

Overview

Editors:
  1. Boris Kovalerchuk

    1. Dept. of Computer Science, Central Washington University, Ellensburg, USA

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  2. Kawa Nazemi

    1. Darmstadt University of Applied Sciences, Darmstadt, Germany

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  3. Răzvan Andonie

    1. Dept. of Computer Science, Central Washington University, Ellensburg, USA

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  4. Nuno Datia

    1. ISEL, Polytechnic Institute of Lisbon, Lisboa, Portugal

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  5. Ebad Bannissi

    1. Department of Informatics, London South Bank University, London, UK

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  • Provides recent research on Artificial Intelligence, Visualization, Visual Knowledge Discovery, and Visual Analytics
  • Is devoted to AI and Visualization for advancing Visual Knowledge Discover
  • Contains extended papers from the International Conference on Information Visualization related to AI

Part of the book series: Studies in Computational Intelligence (SCI, volume 1126)

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About this book

This book continues a series of Springer publications devoted to the emerging field of Integrated Artificial Intelligence and Machine Learning with Visual Knowledge Discovery and Visual Analytics that combine advances in both fields. Artificial Intelligence and Machine Learning face long-standing challenges of explainability and interpretability that underpin trust.  Such attributes are fundamental to both decision-making and knowledge discovery.  Models are approximations and, at best, interpretations of reality that are transposed to algorithmic form.   A visual explanation paradigm is critically important to address such challenges, as current studies demonstrate in salience analysis in deep learning for images and texts.  Visualization means are generally effective for discovering and explaining high-dimensional patterns in all high-dimensional data, while preserving data properties and relations in visualizations is challenging.  Recent developments, such as in General Line Coordinates, open new opportunities to address such challenges.

This book contains extended papers presented in 2021 and 2022 at the International Conference on Information Visualization (IV) on AI and Visual Analytics, with 18 chapters from international collaborators.  The book builds on the previous volume, published in 2022 in the Studies in Computational Intelligence. The current book focuses on the following themes: knowledge discovery with lossless visualizations, AI/ML through visual knowledge discovery with visual analytics case studies application, and visual knowledge discovery in text mining and natural language processing.

The intended audience for this collection includes but is not limited to developers of emerging AI/machine learning and visualization applications, scientists, practitioners, and research students.  It has multiple examples of the current integration of AI/machine learning and visualization for visual knowledge discovery, visual analytics, and text and natural language processing.  The book provides case examples for future directions in this domain.  New researchers find inspiration to join the profession of the field of AI/machine learning through a visualization lens.  

Keywords

Table of contents (18 chapters)

  1. Front Matter

    Pages i-xx
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  2. Visualizing the Unseen: Unleashing Knowledge Discovery with Lossless Visualizations

    1. Front Matter

      Pages 1-1
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    2. Interactive Decision Tree Creation and Enhancement with Complete Visualization for Explainable Modeling

      • Boris Kovalerchuk, Andrew Dunn, Alex Worland, Sridevi Wagle
      Pages 3-40

    3. Full High-Dimensional Intelligible Learning in 2-D Lossless Visualization Space

      • Boris Kovalerchuk, Hoang Phan
      Pages 41-72

    4. Explainable Machine Learning for Categorical and Mixed Data with Lossless Visualization

      • Boris Kovalerchuk, Elijah McCoy
      Pages 73-123

    5. Parallel Coordinates for Discovery of Interpretable Machine Learning Models

      • Dustin Hayes, Boris Kovalerchuk
      Pages 125-158

    6. Visual Knowledge Discovery with General Line Coordinates

      • Lincoln Huber, Boris Kovalerchuk, Charles Recaido
      Pages 159-202

  3. Unveiling Insights: Empowering AI/ML Through Visual Knowledge Discovery

    1. Front Matter

      Pages 203-203
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    2. Search Space Visualizations in the Context of Optimization Problems: A Literature Review

      • Ennio W. L. Silva, Hugo A. D. do Nascimento, Juliana P. Felix, Humberto J. Longo, Bernd Scheuermann, Les Foulds
      Pages 205-222

    3. Relative Confusion Matrix: An Efficient Visualization for the Comparison of Classification Models

      • Luc Etienne Pommé, Romain Bourqui, Romain Giot, David Auber
      Pages 223-243

    4. Computation of Pixel-Oriented Grid Layout for 2D Datasets Using VRGrid

      • Adrien Halnaut, Romain Giot, Romain Bourqui, David Auber
      Pages 245-270

    5. A Method for Comparative Visualization of Labeled Multidimensional Data and Its Application to Machine Learning Data

      • Karen Kosaka, Takayuki Itoh
      Pages 271-290

  4. Illuminating Insights: Visual Analytics Applications and Case Studies

    1. Front Matter

      Pages 291-291
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    2. Road Traffic Flow Prediction with Visual Analytics

      • Nuno Datia, Matilde P. M. Pato, João Vaz, João Moura Pires
      Pages 293-318

    3. Guided Visual Analytics—A Visual Analytics Guidance Approach for Systematic Reviews in Research

      • Lennart B. Sina, Cristian A. Secco, Midhad Blazevic, Kawa Nazemi
      Pages 319-343

    4. Integrating Machine Learning in Visual Analytics for Supporting Collaboration in Science

      • Midhad Blazevic, Lennart B. Sina, Cristian A. Secco, Kawa Nazemi
      Pages 345-373

    5. Visual Knowledge Discovery and Criminal Justice. Insights from a Computational Crime Analysis Research

      • Nicola Lettieri, Alfonso Guarino, Delfina Malandrino, Rocco Zaccagnino
      Pages 375-395

  5. Illuminating Text: Unleashing Knowledge with Visual Discovery in Text Mining and Natural Language Processing

    1. Front Matter

      Pages 397-397
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    2. Designing and Evaluating Context-Sensitive Visualization Models for Deep Learning Text Classifiers

      • Andrew Dunn, Diana Inkpen, Răzvan Andonie
      Pages 399-421

    3. How NLP and Visual Analytics Can Improve Asset Management

      • Pedro Santos, Matilde P. M. Pato, Nuno Datia, José Sobral
      Pages 423-441
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Editors and Affiliations

  • Dept. of Computer Science, Central Washington University, Ellensburg, USA

    Boris Kovalerchuk, Răzvan Andonie

  • Darmstadt University of Applied Sciences, Darmstadt, Germany

    Kawa Nazemi

  • ISEL, Polytechnic Institute of Lisbon, Lisboa, Portugal

    Nuno Datia

  • Department of Informatics, London South Bank University, London, UK

    Ebad Bannissi

Bibliographic Information

  • Book Title: Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery

  • Editors: Boris Kovalerchuk, Kawa Nazemi, Răzvan Andonie, Nuno Datia, Ebad Bannissi

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-031-46549-9

  • Publisher: Springer Cham

  • eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024

  • Hardcover ISBN: 978-3-031-46548-2Published: 25 April 2024

  • Softcover ISBN: 978-3-031-46551-2Due: 26 May 2024

  • eBook ISBN: 978-3-031-46549-9Published: 24 April 2024

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XX, 503

  • Number of Illustrations: 22 b/w illustrations, 258 illustrations in colour

  • Topics: Computational Intelligence, Artificial Intelligence, Data Engineering

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