In the present thriving global economy a need has evolved for complex data analysis to enhance an organization’s 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
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.