Scientific Data Mining and Knowledge Discovery

Principles and Foundations

  • Mohamed Medhat Gaber

Table of contents

  1. Front Matter
    Pages 1-8
  2. Mohamed Medhat Gaber
    Pages 1-4
  3. Background

    1. Front Matter
      Pages 6-6
    2. Achim Hoffmann, Ashesh Mahidadia
      Pages 7-52
    3. Shahjahan Khan
      Pages 53-76
    4. Stephan Grimm
      Pages 111-137
  4. Computational Science

    1. Front Matter
      Pages 140-140
    2. Nafaa Jabeur, Nabil Sahli
      Pages 141-172
    3. Hassan Safouhi, Ahmed Bouferguene
      Pages 173-206
    4. Mohamed Abouelhoda, Moustafa Ghanem
      Pages 207-247
  5. Data Mining and Knowledge Discovery

    1. Front Matter
      Pages 250-250
    2. Nabil Sahli, Nafaa Jabeur
      Pages 251-268
  6. Future Trends

    1. Front Matter
      Pages 344-344
    2. Steve Tanner, Cara Stein, Sara J. Graves
      Pages 345-376
  7. Back Matter
    Pages 1-2

About this book


With the evolution in data storage, large databases have stimulated researchers from many areas, especially machine learning and statistics, to adopt and develop new techniques for data analysis in different fields of science. In particular, there have been notable successes in the use of statistical, computational, and machine learning techniques to discover scientific knowledge in the fields of biology, chemistry, physics, and astronomy. With the recent advances in ontologies and knowledge representation, automated scientific discovery (ASD) has further, great prospects in the future.

The contributions in this book provide the reader with a complete view of the different tools used in the analysis of data for scientific discovery. Gaber has organized the presentation into four parts: Part I provides the reader with the necessary background in the disciplines on which scientific data mining and knowledge discovery are based. Part II details applications of computational methods used in geospatial, chemical, and bioinformatics applications. Part III is about data mining applications in geosciences, chemistry, and physics. Finally, in Part IV, future trends and directions for research are explained.

The book serves as a starting point for students and researchers interested in this multidisciplinary field. It offers both an overview of the state of the art and lists areas and open issues for future research and development.


Data Streams Statistical Inference bioinformatics data analysis data mining evolution knowledge knowledge discovery knowledge representation learning machine learning ontology philosophy science statistics

Editors and affiliations

  • Mohamed Medhat Gaber
    • 1
  1. 1.Caulfield School of, Information TechnologyMonash UniversityCaulfield EastAustralia

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