| Data Mining for Scientific and Engineering Applications | 
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by Robert L. Grossman University of Illinois at Chicago, USA Chandrika Kamath Lawrence Livermore National Laboratory, CA, USA Philip Kegelmeyer Sandia National Laboratories, Livermore, CA, USA Vipin Kumar Army High Performance Computing Research Center (AHPCRC), Minneapolis, MN,USA Raju R. Namburu Army Research Laboratory, Aberdeen Proving Ground, MD, USA Copyright ® 2001 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. Dr.
N. Radhakrishnan On Mining Scientific
Datasets; Understanding High
Dimensional and Large Data Sets: Some Mathematical Challenges and
Opportunities Jagadish Chandra Data Mining at the
Interface of Computer Science and Statistics Mining Large Image
Collections Mining Astronomical
Databases Roberta
M. Humphreys, Juan
E. Cabanela, and Jeffrey Kriessler Searching for Bent-Double
Galaxies in the First Survey Chandrika Kamath,
Erick Cantú-Paz,
Imola K. Fodor
and Nu Ai Tang A Dataspace Infrastructure
for Astronomical Data Robert
Grossman, Emory Creel, Marco Mazzucco, and Roy
Williams Data Mining Applications in
Bioinformatics Mining Residue Contacts in
Proteins KDD Services at the
Goddard Earth Sciences Distributed Archive Center Christopher Lynnes
and Robert
Mack Data Mining in Integrated
Data Access and Data Analysis Systems Ruixin Yang,
Menas Kafatos, Kwang-Su
Yang, and X. Sean Wang Spatial Data Mining for
Classification, Visualisation and Interpretation with Artmap Neural
Network Weiguo
Liu, Sucharita Gopal, and Curtis
Woodcock Real Time Feature
Extraction for the Analysis of Turbulent Flows I.
Marusic, G.V.
Candler, V.
Interrante, P.K. Subbareddy, and A. Moss Data Mining for Turbulent
Flows Evita-Efficient
Visualization and Interrogation of Tera-Scale Data Raghu Machiraju,
James E. Fowler,
David Thompson, Bharat Soni, and Will Schroeder Towards Ubiquitous Mining
of Distributed Data Hillol Kargupta,
Krishnamoorthy Sivakumar, Weiyun Huang, Rajeev
Ayyagari, Rong Chen, Byung-Hoon Park, and Erik Johnson Decomposable Algorithms for
Data Mining Raj Bhatnagar HDDI™:
Hierarchical
Distributed Dynamic Indexing William
M.
Pottenger, Yong-Bin Kim, and Daryl D. Meling Parallel Algorithms for
Clustering High-Dimensional Large-Scale Datasets Harsha Nagesh,
Sanjay Goil, and Alok
Choudhary Efficient Clustering of
Very Large Document Collections Inderjit
S.
Dhillon, James Fan, and Yuqiang
Guan A Scalable Hierarchical
Algorithm for Unsupervised Clustering High-Performance Singular
Value Decomposition David
B. Skillicorn,
and Xiaolan Yang Mining High-Dimensional
Scientific Data Sets Using Singular Value Decomposition Ekaterina
Maltseva, Clara
Pizzuti, and Domenico
Talia Spatial Dependence in Data
Mining James
P. LeSage, and R. Kelley Pace. Sparc: Spatial Association
Rule-Based Classification Jaiwei
Han, Anthony K.H.
Tung, and Jing He What's Spatial About
Spatial Data Mining: Three Case Studies Shashi
Shekhar, Yan Huang, Weili Wu, C.T. Lu, and S. Chawla Predicting Failures in
Event Sequences Mohammed
J. Zaki, Neal Lesh, and Mitsunori Ogihara Efficient Algorithms for
Mining Long Patterns in Scientific Data Sets Ramesh
C. Agarwal, and Charu C.
Aggarwal. Probabilistic Estimation in
Data Mining Edwin
P.D. Pednault, Chidanand Apte. Classification Using
Association Rules: Weaknesses and Enhancements |
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