Includes index.

Section 1. Fundamentals and literature review -- 1. Data mining and business intelligence: a comparative, historical perspective -- 2. The role of data mining for business intelligence in knowledge management -- 3. Data quality for data mining in business intelligence applications: current state and research -- Section 2. Approaches and methodologies -- 4. ASD-BI: an agile methodology for effective integration of data mining in business intelligence -- 5. A proactive approach for BI -- 6. Analyzing customer behavior using online analytical mining (OLAM) -- Section 3. Web and text mining applications -- 7. Web mining for the integration of data mining with business intelligence in web-based decision support systems -- 8. Text-driven reasoning and multi-structured data analytics for business intelligence -- Section 4. Applications to specific domains -- 9. Pre-triage decision support improvement in maternity care by means of data mining -- 10. Business intelligence and nosocomial infection decision making -- 11. A comprehensive workflow for enhancing business bankruptcy prediction -- Section 5. Software issues -- 12. Open source software integrations -- 13. 3rd order analytics demand planning: a collaboration of BI and predictive analytic tools.

Uncovering and analyzing data associated with the current business environment is essential in maintaining a competitive edge. As such, making informed decisions based on this data is crucial to managers across industries. Integration of Data Mining in Business Intelligence Systems investigates the incorporation of data mining into business technologies used in the decision making process. Emphasizing cutting-edge research and relevant concepts in data discovery and analysis, this book is a comprehensive reference source for policymakers, academicians, researchers, students, technology developers, and professionals interested in the application of data mining techniques and practices in business information systems.