Adverse drug reaction mining in pharmacovigilance data using formal concept analysis
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Domain-driven KDD for mining functionally novel rules and linking disjoint medical hypotheses
Knowledge-Based Systems
Multi-agent based analysis of financial data
MMCP'11 Proceedings of the 2011 international conference on Mathematical Modeling and Computational Science
Actionable knowledge discovery and delivery
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
USpan: an efficient algorithm for mining high utility sequential patterns
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Computer Methods and Programs in Biomedicine
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In the present thriving global economy a need has evolved for complex data analysis to enhance an organizations production systems, decision-making tactics, and performance. In turn, data mining has emerged as one of the most active areas in information technologies. Domain Driven Data Mining offers state-of the-art research and development outcomes on methodologies, techniques, approaches and successful applications in domain driven, actionable knowledge discovery. About this book: Enhances the actionability and wider deployment of existing data-centered data mining through a combination of domain and business oriented factors, constraints and intelligence. Examines real-world challenges to and complexities of the current KDD methodologies and techniques. Details a paradigm shift from "data-centered pattern mining" to "domain driven actionable knowledge discovery" for next-generation KDD research and applications. Bridges the gap between business expectations and research output through detailed exploration of the findings, thoughts and lessons learned in conducting several large-scale, real-world data mining business applications Includes techniques, methodologies and case studies in real-life enterprise data mining Addresses new areas such as blog mining Domain Driven Data Mining is suitable for 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.