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 actionsdomain-driven customer analyticsthe role of human intelligence in AKDmaximal pattern-based clusterontology mining Part II focuses on novel KDD domains and the corresponding techniques, exploring the mining of emergent areas and domains such as:social security datacommunity security datagene sequencesmental health informationtraditional Chinese medicine datacancer related datablog datasentiment informationweb dataproceduresmoving object trajectoriesland use mappinghigher education dataflight schedulingalgorithmic asset managementResearchers, 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.