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.
