Next Generation of Data-Mining Applications
1st Edition
Published on 1. April 2005
Book
Hardback
696 pages
978-0-471-65605-0 (ISBN)
Description
* This book presents the next generation of data mining applications based on state-of-the art methodologies and techniques for analyzing enormous quantities of raw data in high-dimension
* Each chapter describes the data mining development process, results, and experiences with new data mining tools and techniques
* Includes twenty-five novel and diverse contributions from experienced and well-respected data mining scientists and practitioners that describe their recent applications using state-of-the-art methods and algorithms
Reviews / Votes
"...the book is a highly authoritative, cohesive and timely exposure to the recent developments, application areas and future trends in DM. This is must reading for a wide audience of practitioners and researchers." (Mathematical Reviews, 2006e) "...a vast and comprehensive collection of chapters..." (Technometrics, November 2005) "All the major applications of data mining...are covered in detail from the leaders in the field. I recommend this text to practitioners and researchers alike." (Computing Reviews.com, July 26, 2005)More details
Edition
1., Auflage
Language
English
Place of publication
Chicester
United Kingdom
Publishing group
John Wiley and Sons Ltd
Target group
Professional and scholarly
Illustrations
Illustrations
Dimensions
Height: 24.2 cm
Width: 16.2 cm
Thickness: 36 mm
Weight
1104 gr
ISBN-13
978-0-471-65605-0 (9780471656050)
Schweitzer Classification
Persons
MEHMED M. KANTARDZIC, PhD, is Associate Professor and Director of the Data Mining Laboratory at the University of Louisville. He is the author of five books in data mining, including Data Mining: Concepts, Models, Methods, and Algorithms (Wiley-IEEE Press), and has published more than 120 articles in refereed journals and conference proceedings.
JOZEF ZURADA, PhD, is an associate professor at the University of Louisville. He is the coauthor of Knowledge Discovery for Business Information Systems and has published numerous articles in refereed journals and conference proceedings.
Content
Trends in data-mining applications : from research labs to fortune 500 companies. 1. Mining wafer fabrication : framework and challenges. 2. Damage detection employing data-mining techniques. 3. Data projection techniques and their application in sensor array data processing. 4. An application of evolutionary and neural data-mining techniques to customer relationship management. 5. Sales opportunity miner : data mining for automatic evaluation of sales opportunity. 6. A fully distributed framework for cost-sensitive data mining. 7. Application of variable precision rough set approach to care driver assessment. 8. Discovery of patterns in earth science data using data mining. 9. An active learning approach to Egeria densa detection in digital imagery. 10. Experiences in mining data from computer simulations. 11. Statistical modeling of large-scale scientific simulation data. 12. Data mining for gene mapping. 13. Data-mining techniques for microarray data analysis. 14. The use of emerging patterns in the analysis of gene expression profiles for the diagnosis and understanding of diseases. 15. Proteomic data analysis : pattern recognition for medical diagnosis and biomarker discovery. 16. Discovering patterns and reference models in the medical domain of isokinetics. 17. Mining the cystic fibrosis data. 18. On learning strategies for topic-specific web crawling. 19. On analyzing web log data : a parallel sequence-mining algorithm. 20. Interactive methods for taxonomy editing and validation. 21. The use of data-mining techniques in operational crime fighting. 22 .Using data mining for intrusion detection. 23. Mining closed and maximal frequent itemsets. 24. Using fractals in data mining. 25 .Genetic search for logic structures in data.