gogo
Amazon cover image
Image from Amazon.com

Next generation of data mining applications [electronic resource] / edited by Mehmed M. Kantardzic, Jozef Zurada.

Contributor(s): Material type: TextTextPublication details: Hoboken, N.J. ; [Great Britain] : Wiley-Interscience, c2005.Description: 1 online resource (xviii, 671 p.) : illISBN:
  • 9780471696650
  • 047169665X
  • 0471656054
  • 9780471656050
Subject(s): Genre/Form: Additional physical formats: Online version:: Next generation of data mining applications.DDC classification:
  • 006.312
Online resources:
Contents:
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.
No physical items for this record

IT Carlow ebook

IEEE ebook

Includes bibliographical references and index.

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.

Powered by Koha