Advances in technology are making massive data sets common in many scientific disciplines, such as astronomy, medical imaging, bio-informatics, combinatorial chemistry, remote sensing, and physics. To find useful information in these data sets, scientists and engineers are turning to data mining techniques. This book is a collection of papers based on the first two in a series of workshops on mining scientific datasets. It illustrates the diversity of problems and application areas that can benefit from data mining, as well as the issues and challenges that differentiate scientific data mining from its commercial counterpart. While the focus of the book is on mining scientific data, the work is of broader interest as many of the techniques can be applied equally well to data arising in business and web applications. Audience: This work would be an excellent text for students and researchers who are familiar with the basic principles of data mining and want to learn more about the application of data mining to their problem in science or engineering.
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First Sentence:
As data mining has gained acceptance in the commercial world, the important role these techniques play in the scientific and engineering domains is increasingly being overlooked. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
candidate dense units, adaptive grid bins, boolean spatial features, lowest support level, class precision recall, join index structure, swirl count, insurance risk modeling, knowledge extraction algorithms, earned exposure, single minsup, value range queries, semantic locality, local hash table, vegetation durability, swirl strength, main memory consumption, swirl value, scientific data mining, average attribute value, neighborhood box, global hash table, spatial data mining, scientific data sets, bin indices Key Phrases - Capitalized Phrases (CAPs): (learn more)
Kluwer Academic Publishers, International Conference, New York, Diamond Eye, John Wiley, Prentice Hall, Hierarchical Distributed Dynamic Indexing, World Wide Web, Fluid Mech, Kelley Pace, Morgan Kaufmann, Academic Press, Boston University, University of Minnesota, Addison Wesley, National Science Foundation, Springer Verlag, Englewood Cliffs, George Karypis, Journal of Molecular Biology, New Jersey, University of California, California Institute of Technology, Cambridge University Press, Computer Society New!
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As data mining has gained acceptance in the commercial world, the important role these techniques play in the scientific and engineering domains is increasingly being overlooked. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
candidate dense units, adaptive grid bins, boolean spatial features, lowest support level, class precision recall, join index structure, swirl count, insurance risk modeling, knowledge extraction algorithms, earned exposure, single minsup, value range queries, semantic locality, local hash table, vegetation durability, swirl strength, main memory consumption, swirl value, scientific data mining, average attribute value, neighborhood box, global hash table, spatial data mining, scientific data sets, bin indices Key Phrases - Capitalized Phrases (CAPs): (learn more)
Kluwer Academic Publishers, International Conference, New York, Diamond Eye, John Wiley, Prentice Hall, Hierarchical Distributed Dynamic Indexing, World Wide Web, Fluid Mech, Kelley Pace, Morgan Kaufmann, Academic Press, Boston University, University of Minnesota, Addison Wesley, National Science Foundation, Springer Verlag, Englewood Cliffs, George Karypis, Journal of Molecular Biology, New Jersey, University of California, California Institute of Technology, Cambridge University Press, Computer Society New!
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Front Cover | Table of Contents | First Pages | Back Cover | Surprise Me!
Citations (learn more)
This book cites 85
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11
books
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- Data Mining for Scientific and Engineering Applications (Massive Computing) by R.L. Grossman on 31 pages
- Data Mining by Jiawei Han on 16 pages
- Classification and Regression Trees by Leo Breiman on 6 pages
- Pattern Classification (2nd Edition) by Richard O. Duda on 5 pages
- Pattern Classification and Scene Analysis by Richard O. Duda on 5 pages
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- Grouping Multidimensional Data: Recent Advances in Clustering by Jacob Kogan in Back Matter
- Advances in Knowledge Discovery and Data Mining: 7th Pacific-Asia Conference, PAKDD 2003. Seoul, Korea, April 30 - May 2, 2003, Proceedings (Lecture ... Notes in Artificial Intelligence) (v. 2637) by Kyu-Young Whang on page 543
- Financial Cryptography and Data Security: 9th International Conference, FC 2005, Roseau, The Commonwealth Of Dominica, February 28 - March 3, 2005, ... Computer Science / Security and Cryptology) by Andrew S. Patrick on page 355
- Information Visualization: Beyond the Horizon by Chaomei Chen on page
- Information Visualization by Chaomei Chen on page 271
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