Knowledge discovery and data mining challenges and realities
Title:
Knowledge discovery and data mining challenges and realities
Author:
Zhu, Xingquan, 1973-
ISBN:
9781599042541
Publication Information:
Hershey, Pa. : IGI Global (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA), c2007.
Physical Description:
electronic texts (xv, 274 p. : ill.) : digital files.
Contents:
Section I. Data Mining in Software Quality Modeling -- 1. Software Quality Modeling with Limited Apriori Defect Data --
Section II. Knowledge Discovery from Genetic and Medical Data -- 2. Genome-Wide Analysis of Epistasis Using Multifactor Dimensionality Reduction: Feature Selection and Construction in the Domain of Human Genetics -- 3. Mining Clinical Trial Data --
Section III. Data Mining in Mixed Media Data -- 4. Cross-Modal Correlation Mining Using Graph Algorithms --
Section IV. Mining Image Data Repository -- 5. Image Mining for the Construction of Semantic-Inference Rules and for the Development of Automatic Image Diagnosis Systems -- 6. A Successive Decision Tree Approach to Mining Remotely Sensed Image Data --
Section V. Data Mining and Business Intelligence -- 7. The Business Impact of Predictive Analytics -- 8. Beyond Classification: Challenges of Data Mining for Credit Scoring --
Section VI. Data Mining and Ontology Engineering -- 9. Semantics Enhancing Knowledge Discovery and Ontology Engineering Using Mining Techniques: A Crossover Review -- 10. Knowledge Discovery in Biomedical Data Facilitated by Domain Ontologies --
Section VII. Traditional Data Mining Algorithms -- 11. Effective Intelligent Data Mining Using Dempster-Shafer Theory -- 12. Outlier Detection Strategy Using the Self-Organizing Map -- 13. Re-Sampling Based Data Mining Using Rough Set Theory -- About the Authors -- Index.
Abstract:
"This book provides a focal point for research and real-world data mining practitioners that advance knowledge discovery from low-quality data; it presents in-depth experiences and methodologies, providing theoretical and empirical guidance to users who have suffered from underlying low-quality data. Contributions also focus on interdisciplinary collaborations among data quality, data processing, data mining, data privacy, and data sharing"--Provided by publisher.
Added Corporate Author:
Electronic Access:
Chapter PDFs via platform: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-59904-252-7