Data mining for engineering applications

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