Scientific Data Mining and Knowledge Discovery

1. Front Matter

   Pages 1-8

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2. Introduction

   • Mohamed Medhat Gaber

   Pages 1-4

3. Background

   1. Front Matter

      Pages 6-6

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   2. Machine Learning

      • Achim Hoffmann, Ashesh Mahidadia

      Pages 7-52

   3. Statistical Inference

      • Shahjahan Khan

      Pages 53-76

   4. The Philosophy of Science and its relation to Machine Learning

      • Jon Williamson

      Pages 77-89

   5. Concept Formation in Scientific Knowledge Discovery from a Constructivist View

      • Wei Peng, John S. Gero

      Pages 91-109

   6. Knowledge Representation and Ontologies

      • Stephan Grimm

      Pages 111-137

4. Computational Science

   1. Front Matter

      Pages 140-140

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   2. Spatial Techniques

      • Nafaa Jabeur, Nabil Sahli

      Pages 141-172

   3. Computational Chemistry

      • Hassan Safouhi, Ahmed Bouferguene

      Pages 173-206

   4. String Mining in Bioinformatics

      • Mohamed Abouelhoda, Moustafa Ghanem

      Pages 207-247

5. Data Mining and Knowledge Discovery

   1. Front Matter

      Pages 250-250

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   2. Knowledge Discovery and Reasoning in Geospatial Applications

      • Nabil Sahli, Nafaa Jabeur

      Pages 251-268

   3. Data Mining and Discovery of Chemical Knowledge

      • Lu Wencong

      Pages 269-317

   4. Data Mining and Discovery of Astronomical Knowledge

      • Ghazi Al-Naymat

      Pages 319-341

6. Future Trends

   1. Front Matter

      Pages 344-344

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   2. On-board Data Mining

      • Steve Tanner, Cara Stein, Sara J. Graves

      Pages 345-376

   3. Data Streams: An Overview and Scientific Applications

      • Charu C. Aggarwal

      Pages 377-397

7. Back Matter

   Pages 1-2

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Mohamed Medhat Gaber “It is not my aim to surprise or shock you – but the simplest way I can summarise is to say that there are now in the world machines that think, that learn and that create. Moreover, their ability to do these things is going to increase rapidly until – in a visible future – the range of problems they can handle will be coextensive with the range to which the human mind has been applied” by Herbert A. Simon (1916-2001) 1Overview This book suits both graduate students and researchers with a focus on discovering knowledge from scienti c data. The use of computational power for data analysis and knowledge discovery in scienti c disciplines has found its roots with the re- lution of high-performance computing systems. Computational science in physics, chemistry, and biology represents the rst step towards automation of data analysis tasks. The rational behind the developmentof computationalscience in different - eas was automating mathematical operations performed in those areas. There was no attention paid to the scienti c discovery process. Automated Scienti c Disc- ery (ASD) [1–3] represents the second natural step. ASD attempted to automate the process of theory discovery supported by studies in philosophy of science and cognitive sciences. Although early research articles have shown great successes, the area has not evolved due to many reasons. The most important reason was the lack of interaction between scientists and the automating systems.

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

• Caulfield School of, Information Technology, Monash University, Caulfield East, Australia

  Mohamed Medhat Gaber

Policies and ethics