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Specificaties
- Objectstaat
- Subtitle
- A Practical Perspective
- EAN
- 9780898716757
- ISBN
- 9780898716757
- Release Year
- 2009
- Book Title
- Scientific Data Mining
- Title
- Scientific Data Mining
- Genre
- Computing & Internet
- Country/Region of Manufacture
- US
- Topic
- Technology & Engineering
- Release Date
- 06/04/2009
Over dit product
Product Identifiers
Publisher
Society for Industrial AND Applied Mathematics
ISBN-10
0898716756
ISBN-13
9780898716757
eBay Product ID (ePID)
84374582
Product Key Features
Number of Pages
304 Pages
Language
English
Publication Name
Scientific Data Mining : a Practical Perspective
Publication Year
2009
Subject
Engineering (General), General, Databases / Data Mining
Type
Textbook
Subject Area
Mathematics, Computers, Technology & Engineering, Science
Format
Trade Paperback
Dimensions
Item Height
0.6 in
Item Weight
19.5 Oz
Item Length
9 in
Item Width
6 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2008-056149
Dewey Edition
22
Illustrated
Yes
Dewey Decimal
502.85/6312
Table Of 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.
Synopsis
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., 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.
LC Classification Number
QA76.9.D343K356 2009
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