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



Researchers, practitioners and university students in the areas of data mining and knowledge discovery, knowledge engineering, human-computer interaction, artificial intelligence, intelligent information processing, decision support systems, knowledge management, and KDD project management are sure to find this a practical and effective means of enhancing their understanding of and using data mining in their own projects.