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[资源] 【分享】Scientific Data Mining_A Practical Perspective.SIAM.2009

Scientific Data Mining_A Practical Perspective
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Authors(Editors):
      Chandrika Kamath
      Lawrence Livermore National Laboratory
      Livermore, California
Publisher: SIAM
Pub Date: 2009
Pages: 304
ISBN: 978-0-898716-75-7

Preface
Advances in sensors, information technology, and high-performance computing have
resulted in massive data sets becoming available in many scientific disciplines. These data
sets are not only very large, being measured in terabytes and petabytes, but are also quite
complex. This complexity arises as the data are collected by different sensors, at different
times, at different frequencies, and at different resolutions. Further, the data are usually in
the form of images or meshes, and often have both a spatial and a temporal component.
These data sets arise in diverse fields such as astronomy, medical imaging, remote sensing,
nondestructive testing, physics, materials science, and bioinformatics. They can be obtained
from simulations, experiments, or observations.
This increasing size and complexity of data in scientific disciplines has resulted in a
challenging problem. Many of the traditional techniques from visualization and statistics
that were used for the analysis of these data are no longer suitable. Visualization techniques,
even for moderate-sized data, are impractical due to their subjective nature and human
limitations in absorbing detail, while statistical techniques do not scale up to massive data
sets. As a result, much of the data collected are never even looked at, and the full potential
of our advanced data collecting capabilities is only partially realized, if at all.
Data mining is the process concerned with uncovering patterns, associations, anomalies,
and statistically significant structures in data. It is an iterative and interactive process
involving data preprocessing, search for patterns, and visualization and validation of
the results. It is a multidisciplinary field, borrowing and enhancing ideas from domains
including image understanding, statistics, machine learning, mathematical optimization,
high-performance computing, information retrieval, and computer vision. Data mining
techniques hold the promise of assisting scientists and engineers in the analysis of massive,
complex data sets, enabling them to make scientific discoveries, gain fundamental insights
into the physical processes being studied, and advance their understanding of the world
around us.

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