Next generation of data mining applications
edited by Mehmed M. Kantardzic, Jozef Zurada.
Bok Engelsk 2005 · Electronic books.
| Omfang | 1 PDF (xviii, 671 pages) : : illustrations.
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| Opplysninger | Bibliographic Level Mode of Issuance: Monograph. - 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.. - Discover the next generation of data-mining tools and technologyThis book brings together an international team of eighty experts to present readers with the next generation of data-mining applications. Unlike other publications that take a strictly academic and theoretical approach, this book features authors who have successfully developed data-mining solutions for a variety of customer types. Presenting their state-of-the-art methodologies and techniques, the authors show readers how they can analyze enormous quantities of data and make new discoveries by connecting key pieces of data that may be spread across several different databases and file servers. The latest data-mining techniques that will revolutionize research across a wide variety of fields including business, science, healthcare, and industry are all presented. Organized by application, the twenty-five chapters cover applications in:. Industry and business . Science and engineering . Bioinformatics and biotechnology . Medicine and pharmaceuticals. Web and text-mining . Security. New trends in data-mining technology. And much more . . .Readers from a variety of disciplines will learn how the next generation of data-mining applications can radically enhance their ability to analyze data and open the doors to new opportunities. Readers will discover:. New data-mining tools to automate the evaluation and qualification of sales opportunities. The latest tools needed for gene mapping and proteomic data analysis. Sophisticated techniques that can be engaged in crime fighting and preventionWith its coverage of the most advanced applications, Next Generation of Data-Mining Applications is essential reading for all researchers working in data mining or who are tasked with making sense of an ever-growing quantity of data. The publication also serves as an excellent textbook for upper-level undergraduate and graduate courses in computer science, information management, and statistics.
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| Emner | |
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| ISBN | 9780471696650
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