Data Mining for Business Applications

Data Mining for Business Applications presents state-of-the-art data mining research and development related to methodologies, techniques, approaches and successful applications. The contributions of this book mark a paradigm shift from data-centered pattern mining to domain-driven actionable knowledge discovery (AKD) for next-generation KDD research and applications. The contents identify how KDD techniques can better contribute to critical domain problems in practice, and strengthen business intelligence in complex enterprise applications. The volume also explores challenges and directions for future data mining research and development in the dialogue between academia and business.Part I centers on developing workable AKD methodologies, including:

• domain-driven data mining
• post-processing rules for actions
• domain-driven customer analytics
• the role of human intelligence in AKD
• maximal pattern-based cluster
• ontology mining

Part II focuses on novel KDD domains and the corresponding techniques, exploring the mining of emergent areas and domains such as:

• social security data
• community security data
• gene sequences
• mental health information
• traditional Chinese medicine data
• cancer related data
• blog data
• sentiment information
• web data
• procedures
• moving object trajectories
• land use mapping
• higher education data
• flight scheduling
• algorithmic asset management