
Scientific Data Mining
A Practical Perspective
Chandrika Kamath(Author)
Society for Industrial & Applied Mathematics,U.S. (Publisher)
Will be published approx. on 30. April 2009
Book
Paperback/Softback
304 pages
978-0-89871-675-7 (ISBN)
Description
Technological advances are enabling scientists to collect vast amounts of data in fields such as medicine, remote sensing, astronomy, and high-energy physics. These data arise not only from experiments and observations, but also from computer simulations of complex phenomena. They are often complex, with both spatial and temporal components. As a result, it has become impractical to manually explore, analyze, and understand the data. Scientific Data Mining: A Practical Perspective describes how techniques from the multi-disciplinary field of data mining can be used to address the modern problem of data overload in science and engineering domains.
Starting with a survey of analysis problems in different applications, this book identifies the common themes across these domains and uses them to define an end-to-end process of scientific data mining. This multi-step process includes tasks such as processing the raw image or mesh data to identify objects of interest;extracting relevant features describing the objects; detecting patterns among the objects; and displaying the patterns for validation by the scientists.
A majority of the book describes how techniques from disciplines such as image and video processing, statistics, machine learning, pattern recognition, and mathematical optimization can be used for the tasks in each step. It also includes a description of software systems developed for scientific data mining; general guidelines for getting started on the analysis of massive, complex data sets; and an extensive bibliography.
Starting with a survey of analysis problems in different applications, this book identifies the common themes across these domains and uses them to define an end-to-end process of scientific data mining. This multi-step process includes tasks such as processing the raw image or mesh data to identify objects of interest;extracting relevant features describing the objects; detecting patterns among the objects; and displaying the patterns for validation by the scientists.
A majority of the book describes how techniques from disciplines such as image and video processing, statistics, machine learning, pattern recognition, and mathematical optimization can be used for the tasks in each step. It also includes a description of software systems developed for scientific data mining; general guidelines for getting started on the analysis of massive, complex data sets; and an extensive bibliography.
More details
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 258 mm
Width: 182 mm
Thickness: 14 mm
Weight
531 gr
ISBN-13
978-0-89871-675-7 (9780898716757)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Person
Chandrika Kamath is a researcher at Lawrence Livermore National Laboratory, where she is involved in the analysis of data from scientific simulations, observations, and experiments. Her interests include signal and image processing, machine learning, pattern recognition, and statistics, as well as the application of data mining techniques to the solution of practical problems.
Content
Preface
Chapter 1: Introduction
Chapter 2: Data Mining in Science and Engineering
Chapter 3: Common Themes in Mining Scientific Data
Chapter 4: The Scientific Data Mining Process
Chapter 5: Reducing the Size of the Data
Chapter 6: Fusing Different Data Modalities
Chapter 7: Enhancing Image Data
Chapter 8: Finding Objects in the Data
Chapter 9: Extracting Features Describing the Objects
Chapter 10: Reducing the Dimension of the Data
Chapter 11: Finding Patterns in the Data
Chapter 12: Visualizing the Data and Validating the Results
Chapter 13: Scientific Data Mining Systems
Chapter 14: Lessons Learned, Challenges, and Opportunities
Bibliography
Index.
Chapter 1: Introduction
Chapter 2: Data Mining in Science and Engineering
Chapter 3: Common Themes in Mining Scientific Data
Chapter 4: The Scientific Data Mining Process
Chapter 5: Reducing the Size of the Data
Chapter 6: Fusing Different Data Modalities
Chapter 7: Enhancing Image Data
Chapter 8: Finding Objects in the Data
Chapter 9: Extracting Features Describing the Objects
Chapter 10: Reducing the Dimension of the Data
Chapter 11: Finding Patterns in the Data
Chapter 12: Visualizing the Data and Validating the Results
Chapter 13: Scientific Data Mining Systems
Chapter 14: Lessons Learned, Challenges, and Opportunities
Bibliography
Index.