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【分享】Scientific Data Mining_A Practical Perspective.SIAM.2009
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Scientific Data Mining_A Practical Perspective 免责声明 本资源来自于互联网，仅供网络测试之用，请务必在下载后24小时内删除！所有资源不涉及任何商业用途。发帖人不承担由下载使用者引发的一切法律责任及连带责任！ 著作权归原作者或出版社所有。未经发贴人conanwj许可，严禁任何人以任何形式转贴本文，违者必究！ 如果本帖侵犯您的著作权，请与conanwj联系，收到通知后我们将立即删除此帖！ 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. 本资源共3个可选网络硬盘链接，2.65 MB。 -------------------------------------------------------------------------------------------------------- https://www.easy-share.com/1910523135/Scientific Data Mining_A Practical Perspective.Chandrika Kamath.p304.9780898716757.SIAM.2009.rar https://rapidshare.com/files/394 ... 16757.SIAM.2009.rar https://www.divshare.com/download/11576563-0c6 https://www.sendspace.com/file/9cow6h -------------------------------------------------------------------------------------------------------- |
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