Copyright � 2001
Kluwer Academic Publishers (Click link for ordering info)
ISBN 1-4020-0033-2
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.
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
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
Real Time Feature Extraction for the Analysis of Turbulent Flows
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
A Scalable Hierarchical Algorithm for Unsupervised Clustering
High-Performance Singular Value Decomposition
Mining High-Dimensional Scientific Data Sets Using Singular Value Decomposition
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
Bing Liu, Yiming Ma, and Ching-Kian Wong