Ebook: Scientific Data Mining and Knowledge Discovery: Principles and Foundations
- Tags: Data Mining and Knowledge Discovery, Computational Science and Engineering, Artificial Intelligence (incl. Robotics), Pattern Recognition, Computer Applications in Chemistry, Computer Applications in Earth Sciences
- Year: 2010
- Publisher: Springer-Verlag Berlin Heidelberg
- Edition: 1
- Language: English
- pdf
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.
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.
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.
Content:
Front Matter....Pages 1-8
Front Matter....Pages 6-6
Machine Learning....Pages 7-52
Statistical Inference....Pages 53-76
The Philosophy of Science and its relation to Machine Learning....Pages 77-89
Concept Formation in Scientific Knowledge Discovery from a Constructivist View....Pages 91-109
Knowledge Representation and Ontologies....Pages 111-137
Front Matter....Pages 140-140
Spatial Techniques....Pages 141-172
Computational Chemistry....Pages 173-206
String Mining in Bioinformatics....Pages 207-247
Front Matter....Pages 250-250
Knowledge Discovery and Reasoning in Geospatial Applications....Pages 251-268
Data Mining and Discovery of Chemical Knowledge....Pages 269-317
Data Mining and Discovery of Astronomical Knowledge....Pages 319-341
Front Matter....Pages 344-344
On-board Data Mining....Pages 345-376
Data Streams: An Overview and Scientific Applications....Pages 377-397
Introduction....Pages 1-4
Back Matter....Pages 1-2
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.
Content:
Front Matter....Pages 1-8
Front Matter....Pages 6-6
Machine Learning....Pages 7-52
Statistical Inference....Pages 53-76
The Philosophy of Science and its relation to Machine Learning....Pages 77-89
Concept Formation in Scientific Knowledge Discovery from a Constructivist View....Pages 91-109
Knowledge Representation and Ontologies....Pages 111-137
Front Matter....Pages 140-140
Spatial Techniques....Pages 141-172
Computational Chemistry....Pages 173-206
String Mining in Bioinformatics....Pages 207-247
Front Matter....Pages 250-250
Knowledge Discovery and Reasoning in Geospatial Applications....Pages 251-268
Data Mining and Discovery of Chemical Knowledge....Pages 269-317
Data Mining and Discovery of Astronomical Knowledge....Pages 319-341
Front Matter....Pages 344-344
On-board Data Mining....Pages 345-376
Data Streams: An Overview and Scientific Applications....Pages 377-397
Introduction....Pages 1-4
Back Matter....Pages 1-2
....